Eight to Late

From inactivism to interactivism – managerial attitudes to planning

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Introduction

Managers display a range of attitudes towards planning for the future.  In an essay entitled Systems, Messes and Interactive Planning, the management guru/philosopher Russell Ackoff classified attitudes to organizational planning into four distinct types which I describe in detail below. I suspect you may recognise examples of each of these in your organisation…indeed, you might even see shades of yourself :-)

Inactivism

This attitude, as its name suggests, is characterized by a lack of meaningful action. Inactivism is often displayed by managers in organisations that favour the status quo.  These organisations are happy with the way things are, and therefore see no need to change. However, lack of meaningful action does not mean lack of action. On the contrary, it often takes a great deal of effort to fend off change and keep things the way they are. As Ackoff states:

Inactive organizations require a great deal of activity to keep changes from being made. They accomplish nothing in a variety of ways. First, they require that all important decisions be made “at the top.” The route to the top is deliberately designed like an obstacle course. This keeps most recommendations for change from ever getting there. Those that do are likely to have been delayed enough to make them irrelevant when they reach their destination. Those proposals that reach the top are likely to be farther delayed, often by being sent back down or out for modification or evaluation. The organization thus behaves like a sponge and is about as active…

The inactive manager spends a lot of time and effort in ensuring that things remain the way they are. Hence they act only when a stituation forces them to. Ackoff puts it in his inimitable way by stating that, “Inactivist  managers tend to want what they get rather than get what they want.”

Reactivism

Reactivist managers are a step worse than inactivists  because they believe that disaster is already upon them. This is the type of manager who hankers after the “golden days of yore when things were much better than they are today.” As a result of their deep unease of where they are now, they may try to undo the status quo.  As Ackoff points out, unlike inactivists, reactivists do not ride the tide but try to swim against it.

Typically reactivist managers are wary of technology and new concepts. Moreover, they tend to give more importance to seniority and experience rather than proven competence. They also tend to be fans of simplistic solutions to complex problems…like “solving” the problem of a behind-schedule software project by throwing more people at it.

Preactivism

Preactivists are the opposite of reactivists in that they believe the future is going to be better than the past. Consequently, their efforts are geared towards understanding what the future will look like and how they can prepare for it.  Typically, preactive managers are concerned with facts, figures and forecasts; they are firm believers in scientific planning methods that they have learnt in management schools. As such, one might say that this is the most common species of manager in present  day organisations. Those who are not natural preactivists will fly the preactivist flag when they’re asked for their opinions by their managers because it’s the expected answer.

A key characteristic of preactivist managers is that they tend to revel in creating plans rather than implementing them. As Ackoff puts it, “Preactivists see planning as a sequence of discrete steps which terminate with acceptance or rejection of their plans. What happens to their plans is the responsibility of others.

Interactivism

Interactivists planners are not satisfied with the present, but unlike reactivists or preactivists, they do not hanker for the past, nor do they believe the future is automatically going to be better. They do want to make things better than they were or currently are, but they are continually adjusting their plans for the future by learning from and responding to events.  In short, they believe they can shape the future by their actions.

Experimentation is the hallmark of interactivists.  They are willing to try different approaches and learn from them. Although they believe in learning by experience, they do not want to wait for experiences to happen; they would rather induce them by (often small-scale) experimentation.

Ackoff labels interactivists as idealisers – people who pursue ideals they know cannot be achieved, but can be approximated or even reformulated in the light of new knowledge. As he puts it:

They treat ideals as relative absolutes: ultimate objectives whose formulation depends on our current knowledge and understanding of ourselves and our environment. Therefore, they require continuous reformulation in light of what we learn from approaching them.

To use a now fashionable term, interactivists are intrapreneurs.

Discussion

Although Ackoff shows a clear bias towards  interactivists in his article, he does mention that specific situations may call for other types of planners. As he puts it:

Despite my obvious bias in my characterization of these four postures, there are circumstances in which each is most appropriate. Put simply, if the internal and external dynamics of a system (the tide) are taking one where one wants to go and are doing so quickly enough, inactivism is appropriate. If the direction of change is right but the movement is too slow, preactivism is appropriate. If the change is taking one where one does not want to go and one prefers to stay where one is or was, reactivism is appropriate. However, if one is not willing to settle for the past, the present or the future that appears likely now, interactivism is appropriate.

The key point he makes is that inactivists and preactivists treat planning as a ritual because they see the future as something they cannot change. They can only plan for it (and hope for the best). Interactivists, on the other hand, look for opportunities to influence events and thus potentially change the future. Although both preactivists and interactivists are forward-looking, interactivists tend to be long-term thinkers as compared to preactivists who are more concerned about the short to medium term future.

Conclusion

Ackoff’s classification of planners in organisations is interesting because it highlights the kind of future-focused attitude that managers ought to take.  The sad fact, though, is that a significant number of managers are myopic preactivists, focused on this year’s performance targets rather than what their organisations might look like five or even ten years down the line. This is not the fault of individuals, though. The blame for the undue prevalence of myopic preactivism can be laid squarely on the deep-seated management dogma that rewards short-termism.

Written by K

August 20, 2015 at 9:30 pm

A gentle introduction to cluster analysis using R

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Introduction

Welcome to the second part of my introductory series on text analysis using R (the first article can be accessed here).  My aim in the present piece is to provide a  practical introduction to cluster analysis. I’ll begin with some background before moving on to the nuts and bolts of clustering. We have a fair bit to cover, so let’s get right to it.

A common problem when analysing large collections of documents is to categorize them in some meaningful way. This is easy enough if one has a predefined classification scheme that is known to fit the collection (and if the collection is small enough to be browsed manually). One can then simply scan the documents, looking for keywords appropriate to each category and classify the documents based on the results. More often than not, however, such a classification scheme is not available and the collection too large. One then needs to use algorithms that can classify documents automatically based on their structure and content.

The present post is a practical introduction to a couple of automatic text categorization techniques, often referred to as clustering algorithms.  As the Wikipedia article on clustering tells us:

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

As one might guess from the above, the results of clustering depend rather critically on the method one uses to group objects. Again, quoting from the Wikipedia piece:

Cluster analysis itself is not one specific algorithm, but the general task to be solved. It can be achieved by various algorithms that differ significantly in their notion of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small distances [Note: we’ll use distance-based methods] among the cluster members, dense areas of the data space, intervals or particular statistical distributions [i.e. distributions of words within documents and the entire collection].

…and a bit later:

…the notion of a “cluster” cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms. There is a common denominator: a group of data objects. However, different researchers employ different cluster models, and for each of these cluster models again different algorithms can be given. The notion of a cluster, as found by different algorithms, varies significantly in its properties. Understanding these “cluster models” is key to understanding the differences between the various algorithms.

An upshot of the above is that it is not always straightforward to interpret the output of clustering algorithms. Indeed, we will see this in the example discussed below.

With that said for an introduction, let’s move on to the nut and bolts of clustering.

Preprocessing the corpus

In this section I cover the steps required to create the R objects necessary in order to do clustering. It goes over territory that I’ve covered in detail in the first article in this series – albeit with a few tweaks, so you may want to skim through even if you’ve read my previous piece.

To begin with I’ll assume you have R and RStudio (a free development environment for R) installed on your computer and are familiar with the basic functionality in the text mining ™ package.  If you need help with this, please look at the instructions in my previous article on text mining.

As in the first part of this series,  I will use 30 posts from my blog as the example collection (or corpus, in text mining-speak). The corpus can be downloaded here. For completeness, I will run through the entire sequence of steps – right from loading the corpus into R, to running the two clustering algorithms.

Ready? Let’s go…

The first step is to fire up RStudio and navigate to the directory in which you have unpacked the example corpus. Once this is done, load the text mining package, tm.  Here’s the relevant code (Note: a complete listing of the code in this article can be accessed here):

getwd()
[1] “C:/Users/Kailash/Documents”

#set working directory – fix path as needed!
setwd(“C:/Users/Kailash/Documents/TextMining”)
#load tm library
library(tm)

Loading required package: NLP

Note: R commands are in blue, output in black or red; lines that start with # are comments.

If you get an error here, you probably need to download and install the tm package. You can do this in RStudio by going to Tools > Install Packages and entering “tm”. When installing a new package, R automatically checks for and installs any dependent packages.

The next step is to load the collection of documents into an object that can be manipulated by functions in the tm package.

#Create Corpus
docs <- Corpus(DirSource(“C:/Users/Kailash/Documents/TextMining”))
#inspect a particular document
writeLines(as.character(docs[[30]]))

…<output not shown>

The next step is to clean up the corpus. This includes things such as transforming to a consistent case, removing non-standard symbols & punctuation, and removing numbers (assuming that numbers do not contain useful information, which is the case here):

#Transform to lower case
docs <- tm_map(docs,content_transformer(tolower))
#remove potentiallyy problematic symbols
toSpace <- content_transformer(function(x, pattern) { return (gsub(pattern, ” “, x))})
docs <- tm_map(docs, toSpace, “-“)
docs <- tm_map(docs, toSpace, “:”)
docs <- tm_map(docs, toSpace, “‘”)
docs <- tm_map(docs, toSpace, “•”)
docs <- tm_map(docs, toSpace, “•    “)
docs <- tm_map(docs, toSpace, ” -“)
docs <- tm_map(docs, toSpace, ““”)
docs <- tm_map(docs, toSpace, “””)
#remove punctuation
docs <- tm_map(docs, removePunctuation)
#Strip digits
docs <- tm_map(docs, removeNumbers)

Note: please see my previous article for more on content_transformer and the toSpace function defined above.

Next we remove stopwords – common words (like “a” “and” “the”, for example) and eliminate extraneous whitespaces.

#remove stopwords
docs <- tm_map(docs, removeWords, stopwords(“english”))
#remove whitespace
docs <- tm_map(docs, stripWhitespace)
writeLines(as.character(docs[[30]]))

flexibility eye beholder action increase organisational flexibility say redeploying employees likely seen affected move constrains individual flexibility dual meaning characteristic many organizational platitudes excellence synergy andgovernance interesting exercise analyse platitudes expose difference espoused actual meanings sign wishing many hours platitude deconstructing fun

At this point it is critical to inspect the corpus because  stopword removal in tm can be flaky. Yes, this is annoying but not a showstopper because one can remove problematic words manually once one has identified them – more about this in a minute.

Next, we stem the document – i.e. truncate words to their base form. For example, “education”, “educate” and “educative” are stemmed to “educat.”:

docs <- tm_map(docs,stemDocument)

Stemming works well enough, but there are some fixes that need to be done due to my inconsistent use of British/Aussie and US English. Also, we’ll take this opportunity to fix up some concatenations like “andgovernance” (see paragraph printed out above). Here’s the code:

 

docs <- tm_map(docs, content_transformer(gsub),pattern = “organiz”, replacement = “organ”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “organis”, replacement = “organ”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “andgovern”, replacement = “govern”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “inenterpris”, replacement = “enterpris”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “team-“, replacement = “team”)

The next step is to remove the stopwords that were missed by R. The best way to do this  for a small corpus is to go through it and compile a list of words to be eliminated. One can then create a custom vector containing words to be removed and use the removeWords transformation to do the needful. Here is the code (Note:  + indicates a continuation of a statement from the previous line):

myStopwords <- c(“can”, “say”,”one”,”way”,”use”,
+                  “also”,”howev”,”tell”,”will”,
+                  “much”,”need”,”take”,”tend”,”even”,
+                  “like”,”particular”,”rather”,”said”,
+                  “get”,”well”,”make”,”ask”,”come”,”end”,
+                  “first”,”two”,”help”,”often”,”may”,
+                  “might”,”see”,”someth”,”thing”,”point”,
+                  “post”,”look”,”right”,”now”,”think”,”’ve “,
+                  “’re “)
#remove custom stopwords
docs <- tm_map(docs, removeWords, myStopwords)

Again, it is a good idea to check that the offending words have really been eliminated.

The final preprocessing step is to create a document-term matrix (DTM) – a matrix that lists all occurrences of words in the corpus.  In a DTM, documents are represented by rows and the terms (or words) by columns.  If a word occurs in a particular document n times, then the matrix entry for corresponding to that row and column is n, if it doesn’t occur at all, the entry is 0.

Creating a DTM is straightforward– one simply uses the built-in DocumentTermMatrix function provided by the tm package like so:

dtm <- DocumentTermMatrix(docs)
#print a summary
dtm

<<DocumentTermMatrix (documents: 30, terms: 4131)>>
Non-/sparse entries: 13312/110618
Sparsity           : 89%
Maximal term length: 48
Weighting          : term frequency (tf)

This brings us to the end of the preprocessing phase. Next, I’ll briefly explain how distance-based algorithms work before going on to the actual work of clustering.

An intuitive introduction to the algorithms

As mentioned in the introduction, the basic idea behind document or text clustering is to categorise documents into groups based on likeness. Let’s take a brief look at how the algorithms work their magic.

Consider the structure of the DTM. Very briefly, it is a matrix in which the documents are represented as rows and words as columns. In our case, the corpus has 30 documents and 4131 words, so the DTM is a 30 x 4131 matrix.  Mathematically, one can think of this matrix as describing a 4131 dimensional space in which each of the words represents a coordinate axis and each document is represented as a point in this space. This is hard to visualise of course, so it may help to illustrate this via a two-document corpus with only three words in total.

Consider the following corpus:

Document A: “five plus five”

Document B: “five plus six”

These two  documents can be represented as points in a 3 dimensional space that has the words “five” “plus” and “six” as the three coordinate axes (see figure 1).

Figure 1: Documents A and B as points in a 3-word space

Figure 1: Documents A and B as points in a 3-word space

Now, if each of the documents can be thought of as a point in a space, it is easy enough to take the next logical step which is to define the notion of a distance between two points (i.e. two documents). In figure 1 the distance between A and B  (which I denote as D(A,B))is the length of the line connecting the two points, which is simply, the sum of the squares of the differences between the coordinates of the two points representing the documents.

D(A,B) = \sqrt{(2-1)^2 + (1-1)^2+(0-1)^2} = \sqrt 2

Generalising the above to the 4131 dimensional space at hand, the distance between two documents (let’s call them X and Y) have coordinates (word frequencies)  (x_1,x_2,...x_{4131}) and (y_1,y_2,...y_{4131}), then one can define the straight line distance (also called Euclidean distance)  D(X,Y) between them as:

D(X,Y) = \sqrt{(x_1 - y_1)^2+(x_2 - y_2)^2+...+(x_{4131} - y_{4131})^2}

It should be noted that the Euclidean distance that I have described is above is not the only possible way to define distance mathematically. There are many others but it would take me too far afield to discuss them here – see this article for more  (and don’t be put off by the term metric,  a metric  in this context is merely a distance)

What’s important here is the idea that one can define a numerical distance between documents. Once this is grasped, it is easy to understand the basic idea behind how (some) clustering algorithms work – they group documents based on distance-related criteria.  To be sure, this explanation is simplistic and glosses over some of the complicated details in the algorithms. Nevertheless it is a reasonable, approximate explanation for what goes on under the hood. I hope purists reading this will agree!

Finally, for completeness I should mention that there are many clustering algorithms out there, and not all of them are distance-based.

Hierarchical clustering

The first algorithm we’ll look at is hierarchical clustering. As the Wikipedia article on the topic tells us, strategies for hierarchical clustering fall into two types:

Agglomerative: where we start out with each document in its own cluster. The algorithm  iteratively merges documents or clusters that are closest to each other until the entire corpus forms a single cluster. Each merge happens at a different (increasing) distance.

Divisive:  where we start out with the entire set of documents in a single cluster. At each step  the algorithm splits the cluster recursively until each document is in its own cluster. This is basically the inverse of an agglomerative strategy.

The algorithm we’ll use is hclust which does agglomerative hierarchical clustering. Here’s a simplified description of how it works:

  1. Assign each document to its own (single member) cluster
  2. Find the pair of clusters that are closest to each other and merge them. So you now have one cluster less than before.
  3. Compute distances between the new cluster and each of the old clusters.
  4. Repeat steps 2 and 3 until you have a single cluster containing all documents.

We’ll need to do a few things before running the algorithm. Firstly, we need to convert the DTM into a standard matrix which can be used by dist, the distance computation function in R (the DTM is not stored as a standard matrix). We’ll also shorten the document names so that they display nicely in the graph that we will use to display results of hclust (the names I have given the documents are just way too long). Here’s the relevant code:

#convert dtm to matrix
m <- as.matrix(dtm)>
#write as csv file (optional)
write.csv(m,file=”dtmEight2Late.csv”)
#shorten rownames for display purposes> rownames(m) <- paste(substring(rownames(m),1,3),rep(“..”,nrow(m)),
+                      substring(rownames(m), nchar(rownames(m))-12,nchar(rownames(m))-4))

 

Next we run hclust. The algorithm offers several options check out the documentation for details. I use a popular option called Ward’s method – there are others, and I suggest you experiment with them  as each of them gives slightly different results making interpretation somewhat tricky (did I mention that clustering is as much an art as a science??). Finally, we visualise the results in a dendogram (see Figure 2 below).

#run hierarchical clustering using Ward’s method
groups <- hclust(d,method=”ward.D”)
#plot dendogram, use hang to ensure that labels fall below tree
plot(groups, hang=-1)

 

Figure 2: Dendogram from hierarchical clustering of corpus

Figure 2: Dendogram from hierarchical clustering of corpus

A few words on interpreting dendrograms for hierarchical clusters: as you work your way down the tree in figure 2, each branch point you encounter is the distance at which a cluster merge occurred. Clearly, the most well-defined clusters are those that have the largest separation; many closely spaced branch points indicate a lack of dissimilarity (i.e. distance, in this case) between clusters. Based on this, the figure reveals that there are 2 well-defined clusters – the first one consisting of the three documents at the right end of the cluster and the second containing all other documents. We can display the clusters on the graph using the rect.hclust function like so:

#cut into 2 subtrees – try 3 and 5
rect.hclust(groups,2)

The result is shown in the figure below.

Figure 3: 2 cluster solution

Figure 3: 2 cluster grouping

The figures 4 and 5 below show the grouping for 3,  and 5 clusters.

Figure 4: 3 cluster solution

Figure 4: 3 cluster grouping

 

 

Figure 5: 5 cluster solution

Figure 5: 5 cluster grouping

I’ll make just one point here: the 2 cluster grouping seems the most robust one as it happens at large distance, and is cleanly separated (distance-wise) from the 3 and 5 cluster grouping. That said, I’ll leave you to explore the ins and outs of hclust on your own and move on to our next algorithm.

K means clustering

In hierarchical clustering we did not specify the number of clusters upfront. These were determined by looking at the dendogram after the algorithm had done its work.  In contrast, our next algorithm – K means –   requires us to define the number of clusters upfront (this number being the “k” in the name). The algorithm then generates k document clusters in a way that ensures the within-cluster distances from each cluster member to the centroid (or geometric mean) of the cluster is minimised.

Here’s a simplified description of the algorithm:

  1. Assign the documents randomly to k bins
  2. Compute the location of the centroid of each bin.
  3. Compute the distance between each document and each centroid
  4. Assign each document to the bin corresponding to the centroid closest to it.
  5. Stop if no document is moved to a new bin, else go to step 2.

An important limitation of the k means method is that the solution found by the algorithm corresponds to a local rather than global minimum (this figure from Wikipedia explains the difference between the two in a nice succinct way). As a consequence it is important to run the algorithm a number of times (each time with a different starting configuration) and then select the result that gives the overall lowest sum of within-cluster distances for all documents.  A simple check that a solution is robust is to run the algorithm for an increasing number of initial configurations until the result does not change significantly. That said, this procedure does not guarantee a globally optimal solution.

I reckon that’s enough said about the algorithm, let’s get on with it using it. The relevant function, as you might well have guessed is kmeans. As always, I urge you to check the documentation to understand the available options. We’ll use the default options for all parameters excepting nstart which we set to 100. We also plot the result using the clusplot function from the cluster library (which you may need to install. Reminder you can install packages via the Tools>Install Packages menu in RStudio)

#k means algorithm, 2 clusters, 100 starting configurations
kfit <- kmeans(d, 2, nstart=100)
#plot – need library cluster
library(cluster)
clusplot(m, kfit$cluster, color=T, shade=T, labels=2, lines=0)

The plot is shown in Figure 6.

Figure 6: principal component plot (k=2)

Figure 6: principal component plot (k=2)

The cluster plot shown in the figure above needs a bit of explanation. As mentioned earlier, the clustering algorithms work in a mathematical space whose dimensionality equals the number of words in the corpus (4131 in our case). Clearly, this is impossible to visualize.  To handle this, mathematicians have invented a dimensionality reduction technique called Principal Component Analysis which reduces the number of dimensions to 2 (in this case) in such a way that the reduced dimensions capture as much of the variability between the clusters as possible (and hence the comment, “these two components explain 69.42% of the point variability” at the bottom of the plot in figure 6)

(Aside  Yes I realize the figures are hard to read because of the overly long names, I leave it to you to fix that. No excuses, you know how…:-))

Running the algorithm and plotting the results for k=3 and 5 yields the figures below.

 

Figure 7: Principal component plot (k=3)

Figure 7: Principal component plot (k=3)

 

 

Figure 8: Principal component plot (k=5)

Figure 8: Principal component plot (k=5)

Choosing k

Recall that the k means algorithm requires us to specify k upfront. A natural question then is: what is the best choice of k? In truth there is no one-size-fits-all answer to this question, but there are some heuristics that might sometimes help guide the choice. For completeness I’ll describe one below even though it is not much help in our clustering problem.

In my simplified description of the k means algorithm I mentioned that the technique attempts to minimise the sum of the distances between the points in a cluster and the cluster’s centroid. Actually, the quantity that is minimised is the total of the within-cluster sum of squares (WSS) between each point and the mean. Intuitively one might expect this quantity to be maximum when k=1 and then decrease as k increases, sharply at first and then less sharply as k reaches its optimal value.

The problem with this reasoning is that it often happens that the within cluster sum of squares never shows a slowing down in decrease of the summed WSS. Unfortunately this is exactly what happens in the case at hand.

I reckon a picture might help make the above clearer. Below is the R code to draw a plot of summed WSS as a function of k for k=2 all the way to 29 (1-total number of documents):

#kmeans – determine the optimum number of clusters (elbow method)
#look for “elbow” in plot of summed intra-cluster distances (withinss) as fn of k
wss <- 2:29
for (i in 2:29) wss[i] <- sum(kmeans(d,centers=i,nstart=25)$withinss)
plot(2:29, wss[2:29], type=”b”, xlab=”Number of Clusters”,ylab=”Within groups sum of squares”)

…and the figure below shows the resulting plot.

Figure 10: WSS as a function of k ("elbow plot")

Figure 10: WSS as a function of k (“elbow plot”)

The plot clearly shows that there is no k for which the summed WSS flattens out (no distinct “elbow”).  As a result this method does not help. Fortunately, in this case  one can get a sensible answer using common sense rather than computation:  a choice of 2 clusters seems optimal because both algorithms yield exactly the same clusters and show the clearest cluster separation at this point (review the dendogram and cluster plots for k=2).

The meaning of it all

Now I must acknowledge an elephant in the room that I have steadfastly ignored thus far. The odds are good that you’ve seen it already….

It is this: what topics or themes do the (two) clusters correspond to?

Unfortunately this question does not have a straightforward answer. Although the algorithms suggest a 2-cluster grouping, they are silent on the topics or themes related to these.   Moreover,  as you will see if you experiment, the results of clustering depend on:

  • The criteria for the construction of the DTM  (see the documentation for DocumentTermMatrix for options).
  • The clustering algorithm itself.

Indeed, insofar as clustering is concerned, subject matter and corpus knowledge is the best way to figure out cluster themes. This serves to reinforce (yet again!) that clustering is as much an art as it is a science.

In the case at hand, article length seems to be an important differentiator between the 2 clusters found by both algorithms. The three articles in the smaller cluster are in the top 4 longest pieces in the corpus.  Additionally, the three pieces are related to sensemaking and dialogue mapping. There are probably other factors as well, but none that stand out as being significant.

The take home lesson is that  is that the results of clustering are often hard to interpret. This should not be surprising – the algorithms cannot interpret meaning, they simply chug through a mathematical optimisation problem.

Conclusion

This brings us to the end of a long ramble through clustering.  We’ve explored the two most common methods:  hierarchical and k means clustering (there are many others available in R, and I urge you to explore them). Apart from providing the detailed steps to do clustering, I have attempted to provide an intuitive explanation of how the algorithms work.  I hope I have succeeded in doing so. As always your feedback would be very welcome.

Finally, I’d like to reiterate an important point:  the results of our clustering exercise do not have a straightforward interpretation, and this is often the case in cluster analysis. Fortunately I can close on an optimistic note. There are other text mining techniques that do a better job in grouping documents based on topics and themes rather than word frequencies alone.   I’ll discuss this in the next article in this series.  Until then, I wish you many enjoyable hours exploring the ins and outs of clustering.

Written by K

July 22, 2015 at 8:53 pm

The façade of expertise

with 2 comments

Introduction

Since the 1980s, intangible assets, such as knowledge, have come to represent an ever-increasing proportion of an organisation’s net worth.  One of the problems associated with treating knowledge as an asset is that it is difficult to codify in its entirety. This is largely because knowledge is context and skill dependent, and these are hard to convey by any means other than experience. This is the well-known tacit versus explicit knowledge problem that I have written about at length elsewhere (see this post and this one, for example).  Although a recent development in knowledge management technology goes some way towards addressing the problem of context, it still looms large and is likely to for a while.

Although the problem mentioned above is well-known, it hasn’t stopped legions of consultants and professional organisations from attempting to codify and sell expertise: management consultancies and enterprise IT vendors being prime examples. This has given rise to the notion of a knowledge-intensive firm, an organization in which most work is said to be of an intellectual nature and where well-educated, qualified employees form the major part of the work force.   However, the slipperiness of knowledge mentioned in the previous paragraph suggests that the notion of a knowledge intensive firm (and, by implication, expertise) is problematic. Basically, if it is true that knowledge itself is elusive, and hard-to-codify, it raises the question as to what exactly such firms (and their employees) sell.

In this post, I shed some light on this question by drawing on an interesting paper by Mats Alvesson entitled, Knowledge Work: Ambiguity, Image and Identity (abstract only), as well as my experiences in dealing with IT services and consulting firms.

Background: the notion of a knowledge-intensive firm

The first point to note is that the notion of a knowledge-intensive firm is not particularly precise. Based on the definition offered above, it is clear that a wide variety of organisations may be classified as knowledge intensive firms. For example, management consultancies and enterprise software companies would fall into this category, as would law, accounting and research & development firms.  The same is true of the term knowledge work(er).

One of the implications of the vagueness of the term is that any claim to being a knowledge-intensive firm or knowledge worker can be contested. As Alvesson states:

It is difficult to substantiate knowledge-intensive companies and knowledge workers as distinct, uniform categories. The distinction between these and non- (or less) knowledge-intensive organization/non-knowledge   workers is not self-evident, as all organizations and work  involve “knowledge” and any evaluation of “intensiveness” is likely to be contestable. Nevertheless,  there are, in many crucial respects, differences  between many professional service and high-tech companies on the one hand, and more routinized service and industry companies on the other, e.g. in terms of broadly socially shared ideas about the significance of a long theoretical education and intellectual capacities for the work. It makes sense to refer to knowledge-intensive companies as a vague but meaningful category, with sufficient heuristic value to be useful. The category does not lend itself to precise definition or delimitation and it includes organizations which are neither unitary nor unique. Perhaps the claim to knowledge-intensiveness is one of the most distinguishing features…

The last line in the excerpt is particularly interesting to me because it resonates with my experience: having been through countless IT vendor and management consulting briefings on assorted products and services, it is clear that a large part of their pitch is aimed at establishing their credibility as experts in the field, even though they may not actually be so.

The ambiguity of knowledge work

Expertise in skill-based professions is generally unambiguous – an incompetent pilot will be exposed soon enough. In knowledge work, however, genuine expertise is often not so easily discernable. Alvesson highlights a number of factors that make this so.

Firstly, much of the day-to-day work of knowledge workers such as management consultants and IT experts involves routine matters – meetings, documentation etc. – that do not make great demands on their skills. Moreover, even when involved in one-off tasks such as projects, these workers are generally assigned tasks that they are familiar with. In general, therefore, the nature of their work requires them to follow already instituted processes and procedures.  A somewhat unexpected consequence of this is that incompetence can remain hidden for a long time.

A second issue is that the quality of so-called knowledge work is often hard to evaluate – indeed evaluations may require the engagement of independent experts! This is true even of relatively mundane expertise-based work. As Alvesson states:

Comparisons of the decisions of expert and novice auditors indicate no relationship  between the degree of expertise  (as indicated by experience)  and consensus; in high-risk and less standard situations, the experts’ consensus level was lower than that of novices. [An expert remarked that] “judging the quality of an audit is an extremely problematic exercise” and says that consumers of the audit service “have only a very limited insight into the quality of work undertaken by an audit firm”.

This is true of many different kinds of knowledge work.  As Alvesson tells us:

How can anyone tell whether a headhunting firm has found and recruited the best possible candidates or not…or if an audit has been carried out in a high-quality way?  Or  if  the  proposal by  strategic management consultants is optimal or even helpful, or not. Of course, sometimes one may observe whether something works or not (e.g. after the intervention of a plumber), but normally the issues concerned are not that simple in the context in which the concept of knowledge-intensiveness is frequently used. Here we are mainly dealing with complex and intangible phenomena.  Even if something seems to work, it might have worked even better or the cost of the intervention been much lower if another professional or organization had carried out the task.

In view of the above, it is unlikely that market mechanisms would be effective in sorting out the competent from the incompetent.  Indeed, my experience of dealing with major consulting firms (in IT) leads me believe that market mechanisms tend to make them clones of each other, at least in terms of their offerings and approach. This may be part of the reason why client firms tend to base their contracting decisions on the basis of cost or existing relationships – it makes sense to stick with the known, particularly when the alternatives offer choices akin to Pepsi vs Coke.

But that is not the whole story, experts are often hired for ulterior motives. On the one hand, they  might be hired because they confer legitimacy – “no one ever got fired for hiring McKinsey” is a quote I’ve heard more than a few times in many workplaces. On the other hand, they also make convenient scapegoats when the proverbial stuff hits the fan.

Image cultivation

One of the consequences of the ambiguity of knowledge-intensive work is that employees in such firms are forced to cultivate and maintain the image of being experts, and hence the stereotype of the suited, impeccably-groomed Big 4 consultant. As Alvesson points out, though, image cultivation goes beyond the individual employee:

This image must be  managed on different levels: professional-industrial, corporate and individual. Image may be targeted in specific acts and arrangements,  in visible symbols for public consumption but also in everyday behavior, within the organization and in interaction  with others. Thus image is not just of importance in marketing  and for attracting personnel but also in and after production.  Size and a big name  are  therefore important for  many knowledge-intensive companies – and here we perhaps have a major explanation  for all the mergers and acquisitions  in accounting, management consultancy and  other  professional service companies. A large size is reassuring. A well-known brand name substitutes for difficulties in establishing quality.

Another aspect of image cultivation is the use of rhetoric. Here are some examples taken from the websites of Big 4 consulting firms:

No matter the challenge, we focus on delivering practical and enduring results, and equipping our clients to grow and lead.” —McKinsey

We continue to redefine ourselves and set the bar higher to continually deliver quality for clients, our people, and the society in which we operate.” – Deloitte

Cutting through complexity” – KPMG

Creating value for our clients, people and communities in a changing world” – PWC

Some clients are savvy enough not to be taken in by the platitudinous statements listed above.  However, the fact that knowledge-intensive firms continue to use second-rate rhetoric to attract custom suggests that there are many customers who are easily taken in by marketing slogans.  These slogans are sometimes given an aura of plausibility via case-studies intended to back the claims made. However, more often than not the case studies are based on a selective presentation of facts that depict the firm in the best possible light.

A related point is that such firms often flaunt their current client list in order to attract new clientele. Lines like, “our client list includes 8 of top ten auto manufacturers in the world,” are not uncommon, the unstated implication being that if you are an auto manufacturer, you cannot afford not to engage us. The image cultivation process continues well after the consulting engagement is underway. Indeed, much of a consultant’s effort is directed at ensuring that the engagement will be extended.

Finally, it is important to point out the need to maintain an aura of specialness. Consultants and knowledge workers are valued for what they know. It is therefore in their interest to maintain a certain degree of exclusivity of knowledge. Guilds (such as the Project Management Institute) act as gatekeepers by endorsing the capabilities of knowledge workers through membership criteria based on experience and / or professional certification programs.

Maintaining the façade

Because knowledge workers deal with intangibles, they have to work harder to maintain their identities than those who have more practical skills. They are therefore more susceptible to the vagaries and arbitrariness of organisational life.  As Alvesson notes,

Given the high level of ambiguity and the fluidity of organizational  life and interactions with external actors, involving a strong dependence on somewhat arbitrary evaluations  and opinions of others, many knowledge-intensive workers must struggle more for the accomplishment,  maintenance and gradual change of self-identity, compared to workers whose competence and results are more materially grounded…Compared with people who invest less self- esteem in their work and who have lower expectations,  people in knowledge-intensive  companies are thus vulnerable to frustrations  contingent upon ambiguity of performance  and confirmation.

Knowledge workers are also more dependent on managerial confirmation of their competence and value. Indeed, unlike the case of the machinist or designer, a knowledge worker’s product rarely speaks for itself. It has to be “sold”, first  to management and then (possibly) to the client and the wider world.

The previous paragraphs of this section dealt with individual identity. However, this is not the whole story because organisations also play a key role in regulating the identities of their employees. Indeed, this is how they develop their brand. Alvesson notes four ways in which organisations do this:

  1. Corporate identity – large consulting firms are good examples of this. They regulate the identities of their employees through comprehensive training and acculturation programs. As a board member remarked to me recently, “I like working with McKinsey people, because I was once one myself and I know their approach and thinking processes.”
  2. Cultural programs – these are the near-mandatory organisational culture initiatives in large organisations. Such programs are usually based on a set of “guiding principles” which are intended to inform employees on how they should conduct themselves as employees and representatives of the organisation. As Alvesson notes, these are often more effective than formal structures.
  3. Normalisation – these are the disciplinary mechanisms that are triggered when an employee violates an organisational norm. Examples of this include formal performance management or official reprimands. Typically, though, the underlying issue is rarely addressed. For example, a failed project might result in a reprimand or poor performance review for the project manager, but the underlying systemic causes of failure are unlikely to be addressed…or even acknowledged.
  4. Subjectification – This is where employees mould themselves to fit their roles or job descriptions. A good example of this is when job applicants project themselves as having certain skills and qualities in their resumes and in interviews. If selected, they may spend the first few months in learning and internalizing what is acceptable and what is not. In time, the new behaviours are internalized and become a part of their personalities.

It is clear from the above that maintaining the façade of expertise in knowledge work involves considerable effort and manipulation, and has little to do with genuine knowledge. Indeed, it is perhaps because genuine expertise is so hard to identify that people and organisations strive to maintain appearances.

Conclusion

The ambiguous nature of knowledge requires (and enables!) consultants and technology vendors to maintain a façade of expertise. This is done through a careful cultivation of image via the rhetoric of marketing, branding and impression management.The onus is therefore on buyers to figure out if there’s anything of substance behind words and appearances. The volume of business enjoyed by big consulting firms suggests that this does not happen as often as it should, leading us to the inescapable conclusion that decision-makers in organisations are all too easily deceived by the facade of expertise.

Written by K

July 8, 2015 at 8:47 pm

Catch-22 and the paradoxes of organisational life

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“You mean there’s a catch?”

“Sure there’s a catch”, Doc Daneeka replied. “Catch-22. Anyone who wants to get out of combat duty isn’t really crazy.”

There was only one catch and that was Catch-22, which specified that a concern for one’s own safety in the face of dangers that were real and immediate was the process of a rational mind. Orr was crazy and could be grounded. All he had to do was ask; and as soon as he did, he would no longer be crazy and would have to fly more missions…”   Joseph Heller, Catch-22

Introduction

The term Catch-22 was coined by Joseph Heller in the eponymous satirical novel written in 1961. As the quote above illustrates,  the term refers to a paradoxical situation caused by the application of  contradictory rules.  Catch-22 situations are common in large organisations of all kinds, not just the military (which was the setting of the novel). So much so that it is a theme that has attracted some scholarly attention over the half century since the novel was first published  – see this paper or this one for example.

Although Heller uses Catch-22 situations to highlight the absurdities of bureaucracies in a humorous way, in real-life such situations can be deeply troubling for people who are caught up in them. In a paper published in 1956, the polymath Gregory Bateson and his colleagues  suggested that these situations can cause people to behave in ways that are symptomatic of schizophrenia .  The paper introduces the notion of a  double-bind, which is  a dilemma arising from an individual receiving two or more messages that contradict each other .   In simple terms, then,  a double-bind is a Catch-22.

In this post, I draw on Bateson’s  double bind theory to get some insights into Catch-22 situations in organisations.

Double bind theory

The basic elements of a double bind situation are as follows:

  1. Two or more individuals, one of whom is a victim – i.e. the individual who experiences the dilemma described below.
  2. A primary rule which keeps the victim fearful of the consequences of doing (or not doing) something.  This rule typically takes the form , “If you do x then you will be punished” or “If you do not do x then you will be punished. “
  3. A secondary rule that is in conflict with the primary rule, but at more abstract level. This rule, which is usually implicit, typically takes the form, “Do not question the rationale behind x.”
  4. A tertiary rule that prevents the victim from escaping from the situation.
  5. Repeated experiences of (1) and (2)

A simple example (quoted from this article) serves to illustrate the above in a real- life situation:

One example of double bind communication is a mother giving her child the message: “Be spontaneous” If the child acts spontaneously, he is not acting spontaneously because he is following his mother’s direction. It’s a no-win situation for the child. If a child is subjected to this kind of communication over a long period of time, it’s easy to see how he could become confused.

Here the injunction to “Be spontaneous” is contradicted by the more implicit rule that “one cannot be spontaneous on demand.”  It is important to note that the primary and secondary (implicit) rules are at different logical levels  –  the first is about an action, whereas the second is about the nature of all such actions. This is typical of a double bind situation.

The paradoxical aspects of double binds can sometimes be useful as they can lead to creative solutions arising from the victim “stepping outside the situation”. The following example from Bateson’s paper illustrates the point:

The Zen Master attempts to bring about enlightenment in his pupil in various ways. One of the things he does is to hold a stick over the pupil’s head and say fiercely, “If you say this stick is real, I will strike you with it. If you say this stick is not real, I will strike you with it. If you don’t say anything, I will strike you with it.”… The Zen pupil might reach up and take the stick away from the Master–who might accept this response.

This is an important point which we’ll return to towards the end of  this piece.

Double binds in organisations

Double bind situations are ubiquitous in organisations.   I’ll illustrate this by drawing on a couple of examples I have written about earlier on this blog.

The paradox of learning organisations

This section draws on a post I wrote while ago. In the introduction to that post I stated that:

The term learning organisation refers to an organisation that continually modifies its processes  based on observation and experience, thus adapting to changes in its internal and external environment.   Ever since Peter Senge coined the term in his book, The Fifth Discipline, assorted consultants and academics have been telling us that although a  learning  organisation is an utopian ideal, it is one worth striving for.  The reality, however,  is that most organisations that undertake the journey actually end up in a place far removed  from this ideal. Among other things, the journey may expose managerial hypocrisies that contradict the very notion of a learning organisation.

Starkly put, the problem arises from the fact that in a true learning organisation, employees will  inevitably start to question things that management would rather they didn’t.  Consider the following story, drawn from this paper on which the post is based:

…a multinational company intending to develop itself as a learning organization ran programmes to encourage managers to challenge received wisdom and to take an inquiring approach. Later, one participant attended an awayday, where the managing director of his division circulated among staff over dinner. The participant raised a question about the approach the MD had taken on a particular project; with hindsight, had that been the best strategy? `That was the way I did it’, said the MD. `But do you think there was a better way?’, asked the participant. `I don’t think you heard me’, replied the MD. `That was the way I did it’. `That I heard’, continued the participant, `but might there have been a better way?’. The MD fixed his gaze on the participants’ lapel badge, then looked him in the eye, saying coldly, `I will remember your name’, before walking away.

Of course,  a certain kind of learning  occurred here:  the employee learnt that certain questions were taboo, in stark contrast to the openness that was being preached from the organisational pulpit.  The double bind here is evident:  feel free to question and challenge everything…except what management deems to be out of bounds.  The takeaway for employees is that, despite all the rhetoric of organisational learning, certain things should not  be challenged. I think it is safe to say that this was probably not the kind of learning that was intended by those who initiated the program.

The paradoxes of change

In a post on the  paradoxes of organizational change, I wrote that:

An underappreciated facet of organizational change is that it is inherently paradoxical. For example, although it is well known that such changes inevitably have unintended consequences that are harmful, most organisations continue to implement change initiatives in a manner that assumes  complete controllability with the certainty of achieving solely beneficial outcomes.

As pointed out in this paper, there are three types of paradoxes that can arise when an organisation is restructured. The first is that during the transition, people are caught between the demands of their old and new roles. This is exacerbated by the fact that transition periods are often much longer expected. This paradox of performing in turn leads to a paradox of belonging – people become uncertain about where their loyalties (ought to) lie.

Finally, there is a paradox of organising, which refers to the gap between the rhetoric and reality of change. The paper mentioned above has a couple of nice examples. One study described how,

friendly banter in meetings and formal documentation [promoted] front-stage harmony, while more intimate conversations and unit meetings [intensified] backstage conflict.”  Another spoke of a situation in which, “…change efforts aimed at increasing employee participation [can highlight] conflicting practices of empowerment and control. In particular, the rhetoric of participation may contradict engrained organizational practices such as limited access to information and hierarchical authority for decision making…

Indeed, the gap between the intent and actuality of change initiatives make double binds inevitable.

Discussion

I suspect the situations described above will be familiar to people working in a corporate environment. The question is what can one do if one is on the receiving end of such a Catch 22?

The main thing is to realise that a double-bind arises because one perceives the situation to be so. That is, the person experiencing the situation has chosen to interpret it  as a double bind. To be sure, there are usually factors that influence the choice – things such as job security, for example – but the fact is that it is a choice that can be changed if one sees things in a different light. Escaping the double bind is then a “simple” matter of reframing the situation.

Here is where the notion of mindfulness is particularly relevant. In brief, mindfulness is “the intentional, accepting and non-judgemental focus of one’s attention on the emotions, thoughts and sensations occurring in the present moment.”  As the Zen pupil who takes the stick away from the Master, a calm non-judgemental appraisal of a double-bind situation might reveal possible courses of action that had been obscured because of one’s fears. Indeed, the realization that one has more choices than one thinks is in itself a liberating discovery.

It is important to emphasise that the actual course of action that one selects in the end matters less than the realisation that one’s reactions to such situations is largely under one’s own control.

In closing – reframe it!

Organisational life is rife with Catch 22s. Most of us cannot avoid being caught up in them, but we can choose how we react to them. This is largely a matter of reframing them in ways that open up new avenues for action, a point that brings to mind this paragraph from Catch-22 (the book):

“Why don’t you use some sense and try to be more like me? You might live to be a hundred and seven, too.”

“Because it’s better to die on one’s feet than live on one’s knees,” Nately retorted with triumphant and lofty conviction. “I guess you’ve heard that saying before.”

“Yes, I certainly have,” mused the treacherous old man, smiling again. “But I’m afraid you have it backward. It is better to live on one’s feet than die on one’s knees. That is the way the saying goes.”

“Are you sure?” Nately asked with sober confusion. “It seems to make more sense my way.”

“No, it makes more sense my way. Ask your friends.”

And that, I reckon, is as brilliant an example of reframing as I have ever come across.

Written by K

June 22, 2015 at 9:54 pm

The Risk – a dialogue mapping vignette

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Foreword

Last week, my friend Paul Culmsee conducted an internal workshop in my organisation on the theme of collaborative problem solving. Dialogue mapping is one of the tools  of the tools he introduced during the workshop.  This piece, primarily intended as a follow-up for attendees,  is an introduction to dialogue mapping via a vignette that illustrates its practice (see this post for another one). I’m publishing it here as I thought it might be useful for those who wish to understand what the technique is about.

Dialogue mapping uses a notation called Issue Based Information System (IBIS), which I have discussed at length in this post. For completeness, I’ll begin with a short introduction to the notation and then move on to the vignette.

A crash course in IBIS

The IBIS notation consists of the following three elements:

  1. Issues(or questions): these are issues that are being debated. Typically, issues are framed as questions on the lines of “What should we do about X?” where X is the issue that is of interest to a group. For example, in the case of a group of executives, X might be rapidly changing market condition whereas in the case of a group of IT people, X could be an ageing system that is hard to replace.
  2. Ideas(or positions): these are responses to questions. For example, one of the ideas of offered by the IT group above might be to replace the said system with a newer one. Typically the whole set of ideas that respond to an issue in a discussion represents the spectrum of participant perspectives on the issue.
  3. Arguments: these can be Pros (arguments for) or Cons (arguments against) an issue. The complete set of arguments that respond to an idea represents the multiplicity of viewpoints on it.

Compendium is a freeware tool that can be used to create IBIS maps– it can be downloaded here.

In Compendium, IBIS elements are represented as nodes as shown in Figure 1: issues are represented by blue-green question markspositions by yellow light bulbspros by green + signs and cons by red – signs.  Compendium supports a few other node types, but these are not part of the core IBIS notation. Nodes can be linked only in ways specified by the IBIS grammar as I discuss next.

Figure 1: Elements of IBIS

Figure 1: IBIS node types

The IBIS grammar can be summarized in three simple rules:

  1. Issues can be raised anew or can arise from other issues, positions or arguments. In other words, any IBIS element can be questioned.  In Compendium notation:  a question node can connect to any other IBIS node.
  2. Ideas can only respond to questions– i.e. in Compendium “light bulb” nodes can only link to question nodes. The arrow pointing from the idea to the question depicts the “responds to” relationship.
  3. Arguments  can only be associated with ideas– i.e. in Compendium “+” and “–“  nodes can only link to “light bulb” nodes (with arrows pointing to the latter)

The legal links are summarized in Figure 2 below.

Figure 2: Legal links in IBIS

Figure 2: Legal links in IBIS

 

…and that’s pretty much all there is to it.

The interesting (and powerful) aspect of IBIS is that the essence of any debate or discussion can be captured using these three elements. Let me try to convince you of this claim via a vignette from a discussion on risk.

 The Risk – a Dialogue Mapping vignette

“Morning all,” said Rick, “I know you’re all busy people so I’d like to thank you for taking the time to attend this risk identification session for Project X.  The objective is to list the risks that we might encounter on the project and see if we can identify possible mitigation strategies.”

He then asked if there were any questions. The head waggles around the room indicated there were none.

“Good. So here’s what we’ll do,”  he continued. “I’d like you all to work in pairs and spend 10 minutes thinking of all possible risks and then another 5 minutes prioritising.  Work with the person one your left. You can use the flipcharts in the breakout area at the back if you wish to.”

Twenty minutes later, most people were done and back in their seats.

“OK, it looks as though most people are done…Ah, Joe, Mike have you guys finished?” The two were still working on their flip-chart at the back.

“Yeah, be there in a sec,” replied Mike, as he tore off the flip-chart page.

“Alright,” continued Rick, after everyone had settled in. “What I’m going to do now is ask you all to list your top three risks. I’d also like you tell me why they are significant and your mitigation strategies for them.” He paused for a second and asked, “Everyone OK with that?”

Everyone nodded, except Helen who asked, “isn’t it important that we document the discussion?”

“I’m glad you brought that up. I’ll make notes as we go along, and I’ll do it in a way that everyone can see what I’m writing. I’d like you all to correct me if you feel I haven’t understood what you’re saying. It is important that  my notes capture your issues, ideas and arguments accurately.”

Rick turned on the data projector, fired up Compendium and started a new map.  “Our aim today is to identify the most significant risks on the project – this is our root question”  he said, as he created a question node. “OK, so who would like to start?”

 

 

Fig 3: The root question

Figure 3: The root question

 

“Sure,” we’ll start, said Joe easily. “Our top risk is that the schedule is too tight. We’ll hit the deadline only if everything goes well, and everyone knows that they never do.”

“OK,” said Rick, “as he entered Joe and Mike’s risk as an idea connecting to the root question. “You’ve also mentioned a point that supports your contention that this is a significant risk – there is absolutely no buffer.” Rick typed this in as a pro connecting to the risk. He then looked up at Joe and asked,  “have I understood you correctly?”

“Yes,” confirmed Joe.

 

Fig 4: Map in progress

Figure 4: Map in progress

 

“That’s pretty cool,” said Helen from the other end of the table, “I like the notation, it makes reasoning explicit. Oh, and I have another point in support of Joe and Mike’s risk – the deadline was imposed by management before the project was planned.”

Rick began to enter the point…

“Oooh, I’m not sure we should put that down,” interjected Rob from compliance. “I mean, there’s not much we can do about that can we?”

…Rick finished the point as Rob was speaking.

 

Fig 4: Map in progress

Figure 5: Two pros for the idea

 

“I hear you Rob, but I think  it is important we capture everything that is said,” said Helen.

“I disagree,” said Rob. “It will only annoy management.”

“Slow down guys,” said Rick, “I’m going to capture Rob’s objection as “this is a management imposed-constraint rather than risk. Are you OK with that, Rob?”

Rob nodded his assent.

 

Fig 6: A con enters the picture

Fig 6: A con enters the picture

I think it is important we articulate what we really think, even if we can’t do anything about it,” continued Rick. There’s no point going through this exercise if we don’t say what we really think. I want to stress this point, so I’m going to add honesty  and openness  as ground rules for the discussion. Since ground rules apply to the entire discussion, they connect directly to the primary issue being discussed.”

Figure 7: A "criterion" that applies to the analysis of all risks

Figure 7: A “criterion” that applies to the analysis of all risks

 

“OK, so any other points that anyone would like to add to the ones made so far?” Queried Rick as he finished typing.

He looked up. Most of the people seated round the table shook their heads indicating that there weren’t.

“We haven’t spoken about mitigation strategies. Any ideas?” Asked Rick, as he created a question node marked “Mitigation?” connecting to the risk.

 

Figure 8: Mitigating the risk

Figure 8: Mitigating the risk

“Yeah well, we came up with one,” said Mike. “we think the only way to reduce the time pressure is to cut scope.”

“OK,” said Rick, entering the point as an idea connecting to the “Mitigation?” question. “Did you think about how you are going to do this? He entered the question “How?” connecting to Mike’s point.

Figure 9: Mitigating the risk

Figure 9: Mitigating the risk

 

“That’s the problem,” said Joe, “I don’t know how we can convince management to cut scope.”

“Hmmm…I have an idea,” said Helen slowly…

“We’re all ears,” said Rick.

“…Well…you see a large chunk of time has been allocated for building real-time interfaces to assorted systems – HR, ERP etc. I don’t think these need to be real-time – they could be done monthly…and if that’s the case, we could schedule a simple job or even do them manually for the first few months. We can push those interfaces to phase 2 of the project, well into next year.”

There was a silence in the room as everyone pondered this point.

“You know, I think that might actually work, and would give us an extra month…may be even six weeks for the more important upstream stuff,” said Mike. “Great idea, Helen!”

“Can I summarise this point as – identify interfaces that can be delayed to phase 2?” asked Rick, as he began to type it in as a mitigation strategy. “…and if you and Mike are OK with it, I’m going to combine it with the ‘Cut Scope’ idea to save space.”

“Yep, that’s fine,” said Helen. Mike nodded OK.

Rick deleted the “How?” node connecting to the “Cut scope” idea, and edited the latter to capture Helen’s point.

Figure 10: Mitigating the risk

Figure 10: Mitigating the risk

“That’s great in theory, but who is going to talk to the affected departments? They will not be happy.” asserted Rob.  One could always count on compliance to throw in a reality check.

“Good point,”  said Rick as he typed that in as a con, “and I’ll take the responsibility of speaking to the department heads about this,” he continued entering the idea into the map and marking it as an action point for himself. “Is there anything else that Joe, Mike…or anyone else would like to add here,” he added, as he finished.

Figure 11: Completed discussion of first risk (click to see full size

Figure 11: Completed discussion of first risk (click to view larger image)

“Nope,” said Mike, “I’m good with that.”

“Yeah me too,” said Helen.

“I don’t have anything else to say about this point,” said Rob, “ but it would be great if you could give us a tutorial on this technique. I think it could be useful to summarise the rationale behind our compliance regulations. Folks have been complaining that they don’t understand the reasoning behind some of our rules and regulations. ”

“I’d be interested in that too,” said Helen, “I could use it to clarify user requirements.”

“I’d be happy to do a session on the IBIS notation and dialogue mapping next week. I’ll check your availability and send an invite out… but for now, let’s focus on the task at hand.”

The discussion continued…but the fly on the wall was no longer there to record it.

Afterword

I hope this little vignette illustrates how IBIS and dialogue mapping can aid collaborative decision-making / problem solving by making diverse viewpoints explicit. That said, this is a story, and the problem with stories is that things  go the way the author wants them to.  In real life, conversations can go off on unexpected tangents, making them really hard to map. So, although it is important to gain expertise in using the software, it is far more important to practice mapping live conversations. The latter is an art that requires considerable practice. I recommend reading Paul Culmsee’s series on the practice of dialogue mapping or <advertisement> Chapter 14 of The Heretic’s Guide to Best Practices</advertisement> for more on this point.

That said, there are many other ways in which IBIS can be used, that do not require as much skill. Some of these include: mapping the central points in written arguments (what’s sometimes called issue mapping) and even decisions on personal matters.

To sum up: IBIS is a powerful means to clarify options and lay them out in an easy-to-follow visual format. Often this is all that is required to catalyse a group decision.

A gentle introduction to text mining using R

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Preamble

This article is based on my exploration of the basic text mining capabilities of  R, the open source statistical software. It is intended primarily as a tutorial  for novices in text mining as well as R. However, unlike conventional tutorials,  I spend a good bit of time setting the context by describing the problem that led me to text mining and thence  to R. I also talk about the limitations of  the techniques I describe,  and point out directions for further exploration for those who are interested. Indeed, I’ll likely explore some of these myself in future articles.

If you have no time and /or wish to cut to the chase,  please go straight to the section entitled, Preliminaries – installing R and RStudio. If you have already installed R and have worked with it, you may want to stop reading as I  doubt there’s anything I can tell you that you don’t already know :-)

A couple of warnings are in order before we proceed. R and the text mining options we explore below are open source software. Version differences in open source can be significant and are not always documented in a way that corporate IT types are used to. Indeed, I was tripped up by such differences in an earlier version of this article (now revised). So, just for the record, the examples below were run on version 3.2.0 of R and version 0.6-1 of the tm (text mining) package for R. A second point follows from this: as is evident from its version number, the tm package is still in the early stages of its evolution. As a result – and we will see this below – things do not always work as advertised. So assume nothing, and inspect the results in detail at every step. Be warned that I do not always do this below, as my aim is introduction rather than accuracy.

Background and motivation

Traditional data analysis is based on the relational model in which data is stored in tables. Within tables, data is stored in rows – each row representing a  single record of an entity of interest (such as a customer or an account). The columns represent attributes of the entity. For example, the customer table might consist of columns such as name, street address, city, postcode, telephone number .  Typically these  are defined upfront, when the data model is created. It is possible to add columns after the fact, but this tends to be messy because one also has to update existing rows with information pertaining to the added attribute.

As long as one asks for information that is based  only on existing attributes – an example being,  “give me a list of customers based  in Sydney” –  a database analyst can use Structured Query Language (the defacto language of relational databases ) to  get an answer.  A problem arises, however, if one asks for information that is based on attributes that are not included in the database. An example  in the above case would be: “give me a list of  customers who have made a complaint in the last twelve months.”

As a result of the above, many data modelers will include  a “catch-all”  free text column that can be used to capture additional information in an ad-hoc way. As one might imagine, this column will often end up containing several lines, or even paragraphs of text that are near impossible to analyse with the tools available in relational databases.

(Note: for completeness I should add that most database vendors have incorporated text mining capabilities into their products. Indeed, many  of them now include R…which is another good reason to learn it.)

My story

Over the last few months, when time permits, I’ve been doing an in-depth exploration of  the data captured by my organisation’s IT service management tool.  Such tools capture all support tickets that are logged, and track their progress until they are closed.  As it turns out,  there are a number of cases where calls are logged against categories that are too broad to be useful – the infamous catch-all category called “Unknown.”  In such cases, much of the important information is captured in a free text column, which is difficult to analyse unless one knows what one is looking for. The problem I was grappling with was to identify patterns and hence define sub-categories that would enable support staff to categorise these calls meaningfully.

One way to do this  is  to guess what the sub-categories might be…and one can sometimes make pretty good guesses if one knows the data well enough.  In general, however, guessing is a terrible strategy because one does not know what one does not know. The only sensible way to extract subcategories is to  analyse  the content of the free text column systematically. This is a classic text mining problem.

Now, I knew a bit about the theory of text mining, but had little practical experience with it. So the logical place for me to  start was to look for a suitable text mining tool. Our vendor (who shall remain unnamed) has a “Rolls-Royce” statistical tool that has a good text mining add-on. We don’t have licenses for the tool, but the vendor was willing to give us a trial license for a few months…with the understanding that this was on an intent-to-purchase basis.

I therefore started looking at open source options. While doing so, I stumbled on an interesting paper by Ingo Feinerer that describes a text mining framework for the R environment. Now, I knew about R, and was vaguely aware that it offered text mining capabilities, but I’d not looked into the details.  Anyway, I started reading the paper…and kept going until I finished.

As I read, I realised that this could be the answer to my problems. Even better, it would not require me trade in assorted limbs for a license.

I decided to give it a go.

Preliminaries – installing R and RStudio

R can be downloaded from the R Project website. There is a Windows version available, which installed painlessly on my laptop. Commonly encountered installation issues are answered in the (very helpful)  R for Windows FAQ.

RStudio is an integrated development environment (IDE) for R. There is a commercial version of the product, but there is also a free open source version. In what follows, I’ve used the free version. Like R, RStudio installs painlessly and also detects your R installation.

RStudio has  the following panels:

  • A script editor in which you can create R scripts (top left). You can also open a new script editor window by going to File > New File > RScript.
  • The console where you can execute R commands/scripts (bottom left)
  • Environment and history (top right)
  • Files in the current working directory, installed R packages, plots and a help display screen (bottom right).

Check out this short video for a quick introduction to RStudio.

You can access help anytime (within both R and RStudio) by typing a question mark before a command. Exercise: try this by typing ?getwd() and ?setwd() in the console.

I should reiterate that the installation process for both products was seriously simple…and seriously impressive.  “Rolls-Royce” business intelligence vendors could take a lesson from  that…in addition to taking a long hard look at the ridiculous prices they charge.

There is another small step before we move on to the fun stuff.   Text mining  and certain plotting packages are not installed by default so one has to install them manually The relevant packages are:

  1. tm – the text mining package (see documentation). Also check out this excellent introductory article on tm.
  2. SnowballC – required for stemming (explained below).
  3. ggplot2 – plotting capabilities (see documentation)
  4. wordcloud – which is self-explanatory (see documentation) .

(Warning for Windows users: R is case-sensitive so Wordcloud != wordcloud)

The simplest way to install packages is to use RStudio’s built in capabilities (go to Tools > Install Packages in the menu). If you’re working on Windows 7 or 8, you might run into a permissions issue when installing packages. If you do, you might find this advice from the R for Windows FAQ helpful.

Preliminaries – The example dataset

The data I had from our service management tool isn’t  the best dataset to learn with as it is quite messy. But then, I  have a reasonable data source in my virtual backyard:  this blog. To this end, I converted all posts I’ve written since Dec 2013 into plain text form (30 posts in all). You can download the zip file of these here (Note: In case you’re wondering about the URL (orafusion): WordPress does not allow uploads of zip files so I had to host it on one of my other sites).

I suggest you create a new folder called – called, say, TextMining – and unzip the files in that folder.

That done, we’re good to start…

Preliminaries – Basic Navigation

A few things to note before we proceed:

  • In what follows, I enter the commands directly in the console. However,  here’s a little RStudio tip that you may want to consider: you can enter an R command or code fragment in the script editor and then hit Ctrl-Enter  (i.e. hit the Enter key while holding down the Control key) to copy the line to the console.  This will enable you to save the script as you go along.
  • In the code snippets below, the functions / commands to be typed in the R console are in blue font.   The output is in black. I will also denote references to  functions / commands in the body of the article by italicising them as in “setwd()”.  Be aware that I’ve omitted the command prompt “>” in the code snippets below!
  • It is best not to cut-n-paste commands directly from the article as quotes are sometimes not rendered correctly. A text file of all the code in this article is available here.

The > prompt in the RStudio console indicates that R is ready to process commands.

To see the current working directory type in getwd() and hit return. You’ll see something like:

getwd()
[1] “C:/Users/Documents”

 

The exact output will of course depend on your working directory.  Note the forward slashes in the path. This is because of R’s Unix heritage (backslash is an escape character in R.). So, here’s how would change the working directory to C:\Users: 

setwd(“C:/Users”)

You can now use getwd()to check that setwd() has done what it should.

getwd()
[1]”C:/Users”

 

I won’t say much more here about R  as I want to get on with the main business of the article.  Check out this very short introduction to R for a quick crash course.

Loading data into R

Start RStudio and open the TextMining project you created earlier.

The next step is to load the tm package as this is not loaded by default.  This is done using the library() function like so:

library(tm)
Loading required package: NLP

 

Dependent packages are loaded automatically – in this case the dependency is on the NLP (natural language processing) package.

Next, we need to create a collection of documents (technically referred to as a Corpus) in the R environment. This basically involves loading the files created in the TextMining folder into a Corpus object. The tm package provides the Corpus() function to do this. There are several ways to  create a Corpus (check out the online help using ? as explained earlier). In a nutshell, the  Corpus() function can read from various sources including a directory. That’s the option we’ll use:

#Create Corpus
docs <- Corpus(DirSource(“C:/Users/Kailash/Documents/TextMining”))

 

At the risk of stating the obvious, you will need to tailor this path as appropriate.

A couple of things to note in the above. Any line that starts with a # is a comment, and the “<-“ tells R to assign the result of the command on the right hand side to the variable on the left hand side. In this case the Corpus object created is stored in a variable called docs.  One can also use the equals sign (=)  for assignment if one wants to.

Type in docs to see some information about the newly created corpus:

docs
<<VCorpus>>
Metadata: corpus specific: 0, document level (indexed): 0
Content: documents: 30

 

The summary() function gives more details, including a complete listing of files…but it isn’t particularly enlightening.  Instead, we’ll examine a particular document in the corpus.

#inspect a particular document
writeLines(as.character(docs[[30]]))
…output not shown…

Which prints the entire content of 30th document in the corpus to the console.

Pre-processing

Data cleansing, though tedious, is perhaps the most important step in text analysis.   As we will see, dirty data can play havoc with the results.  Furthermore, as we will also see, data cleaning is invariably an iterative process as there are always problems that are overlooked the first time around.

The tm package offers a number of transformations that ease the tedium of cleaning data. To see the available transformations  type getTransformations() at the R prompt:

> getTransformations()
[1] “removeNumbers” “removePunctuation” “removeWords” “stemDocument” “stripWhitespace”

 

Most of these are self-explanatory. I’ll explain those that aren’t as we go along.

There are a few preliminary clean-up steps we need to do before we use these powerful transformations. If you inspect some documents in the corpus (and you know how to do that now), you will notice that I have some quirks in my writing. For example, I often use colons and hyphens without spaces between the words separated by them. Using the removePunctuation transform  without fixing this will cause the two words on either side of the symbols  to be combined. Clearly, we need to fix this prior to using the transformations.

To fix the above, one has to create a custom transformation. The tm package provides the ability to do this via the content_transformer function. This function takes a function as input, the input function should specify what transformation needs to be done. In this case, the input function would be one that replaces all instances of a character by spaces. As it turns out the gsub() function does just that.

Here is the R code to build the content transformer, which  we will call toSpace:

#create the toSpace content transformer
toSpace <- content_transformer(function(x, pattern) {return (gsub(pattern, ” “, x))})

Now we can use  this content transformer to eliminate colons and hypens like so:

docs <- tm_map(docs, toSpace, “-“)
docs <- tm_map(docs, toSpace, “:”)

 

Inspect random sections f corpus to check that the result is what you intend (use writeLines as shown earlier). To reiterate something I mentioned in the preamble, it is good practice to inspect the a subset of the corpus after each transformation.

If it all looks good, we can now apply the removePunctuation transformation. This is done as follows:

#Remove punctuation – replace punctuation marks with ” “
docs <- tm_map(docs, removePunctuation)

 

Inspecting the corpus reveals that several  “non-standard” punctuation marks have not been removed. These include the special quote marks and a space-hyphen combination. These can be removed using our custom content transformer, toSpace. Note that you might want to copy-n-paste these symbols directly from the relevant text file to ensure that they are accurately represented in toSpace.

docs <- tm_map(docs, toSpace, “‘”)
docs <- tm_map(docs, toSpace, “‘”)
docs <- tm_map(docs, toSpace, ” -“)

 

Inspect the corpus again to ensure that the offenders have been eliminated. This is also a good time to check for any other special symbols that may need to be removed manually.

If all is well, you can move  to the next step which is  to:

  • Convert the corpus to lower case
  • Remove all numbers.

Since R is case sensitive, “Text” is not equal to “text” – and hence the rationale for converting to a standard case.  However, although there is a tolower transformation, it is not a part of the standard tm transformations (see the output of getTransformations() in the previous section). For this reason, we have to convert tolower into a transformation that can handle a corpus object properly. This is done with the help of our new friend, content_transformer.

Here’s the relevant code:

#Transform to lower case (need to wrap in content_transformer)
docs <- tm_map(docs,content_transformer(tolower))

Text analysts are typically not interested in numbers since these do not usually contribute to the meaning of the text. However, this may not always be so. For example, it is definitely not the case if one is interested in getting a count of the number of times a particular year appears in a corpus. This does not need to be wrapped in content_transformer as it is a standard transformation in tm.

#Strip digits (std transformation, so no need for content_transformer)
docs <- tm_map(docs, removeNumbers)

Once again, be sure to inspect the corpus before proceeding.

The next step is to remove common words  from the text. These  include words such as articles (a, an, the), conjunctions (and, or but etc.), common verbs (is), qualifiers (yet, however etc) . The tm package includes  a standard list of such stop words as they are referred to. We remove stop words using the standard removeWords transformation like so:

#remove stopwords using the standard list in tm
docs <- tm_map(docs, removeWords, stopwords(“english”))

 

Finally, we remove all extraneous whitespaces using the stripWhitespace transformation:

#Strip whitespace (cosmetic?)
docs <- tm_map(docs, stripWhitespace)

 

Stemming

Typically a large corpus will contain  many words that have a common root – for example: offer, offered and offering.  Stemming is the process of reducing such related words to their common root, which in this case would be the word offer.

Simple stemming algorithms (such as the one in tm) are relatively crude: they work by chopping off the ends of words. This can cause problems: for example, the words mate and mating might be reduced to mat instead of mate.  That said, the overall benefit gained from stemming more than makes up for the downside of such special cases.

To see what stemming does, let’s take a look at the  last few lines  of the corpus before and after stemming.  Here’s what the last bit looks  like prior to stemming (note that this may differ for you, depending on the ordering of the corpus source files in your directory):

writeLines(as.character(docs[[30]]))
flexibility eye beholder action increase organisational flexibility say redeploying employees likely seen affected move constrains individual flexibility dual meaning characteristic many organizational platitudes excellence synergy andgovernance interesting exercise analyse platitudes expose difference espoused actual meanings sign wishing many hours platitude deconstructing fun 

 

Now let’s stem the corpus and reinspect it.

#load library
library(SnowballC)
#Stem document
docs <- tm_map(docs,stemDocument)
writeLines(as.character(docs[[30]]))
flexibl eye behold action increas organis flexibl say redeploy employe like seen affect move constrain individu flexibl dual mean characterist mani organiz platitud excel synergi andgovern interest exercis analys platitud expos differ espous actual mean sign wish mani hour platitud deconstruct fun

 

A careful comparison of the two paragraphs reveals the benefits and tradeoff of this relatively crude process.

There is a more sophisticated procedure called lemmatization that takes grammatical context into account. Among other things, determining the lemma of a word requires a knowledge of its part of speech (POS) – i.e. whether it is a noun, adjective etc. There are POS taggers that automate the process of tagging terms with their parts of speech. Although POS taggers are available for R (see this one, for example), I will not go into this topic here as it would make a long post even longer.

On another important note, the output of the corpus also shows up a problem or two. First, organiz and organis are actually variants of the same stem organ. Clearly, they should be merged. Second, the word andgovern should be separated out into and and govern (this is an error in the original text).  These (and other errors of their ilk) can and should be fixed up before proceeding.  This is easily done using gsub() wrapped in content_transformer. Here is the code to  clean up these and a few other issues  that I found:

docs <- tm_map(docs, content_transformer(gsub), pattern = “organiz”, replacement = “organ”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “organis”, replacement = “organ”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “andgovern”, replacement = “govern”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “inenterpris”, replacement = “enterpris”)
docs <- tm_map(docs, content_transformer(gsub), pattern = “team-“, replacement = “team”)

 

Note that I have removed the stop words and and in in the 3rd and 4th transforms above.

There are definitely other errors that need to be cleaned up, but I’ll leave these for you to detect and remove.

The document term matrix

The next step in the process is the creation of the document term matrix  (DTM)– a matrix that lists all occurrences of words in the corpus, by document. In the DTM, the documents are represented by rows and the terms (or words) by columns.  If a word occurs in a particular document, then the matrix entry for corresponding to that row and column is 1, else it is 0 (multiple occurrences within a document are recorded – that is, if a word occurs twice in a document, it is recorded as “2” in the relevant matrix entry).

A simple example might serve to explain the structure of the TDM more clearly. Assume we have a simple corpus consisting of two documents, Doc1 and Doc2, with the following content:

Doc1: bananas are good

Doc2: bananas are yellow

The DTM for this corpus would look like:

bananas are yellow good
Doc1 1 1 1 0
Doc2 1 1 0 1

 

Clearly there is nothing special about rows and columns – we could just as easily transpose them. If we did so, we’d get a term document matrix (TDM) in which the terms are rows and documents columns. One can work with either a DTM or TDM. I’ll use the DTM in what follows.

There are a couple of general points worth making before we proceed. Firstly, DTMs (or TDMs) can be huge – the dimension of the matrix would be number of document  x the number of words in the corpus.  Secondly, it is clear that the large majority of words will appear only in a few documents. As a result a DTM is invariably sparse – that is, a large number of its entries are 0.

The business of creating a DTM (or TDM) in R is as simple as:

dtm <- DocumentTermMatrix(docs)

 

This creates a term document matrix from the corpus and stores the result in the variable dtm. One can get summary information on the matrix by typing the variable name in the console and hitting return:

dtm
<<DocumentTermMatrix (documents: 30, terms: 4209)>>
Non-/sparse entries: 14252/112018
Sparsity : 89%
Maximal term length: 48
Weighting : term frequency (tf)

 

This is a 30 x 4209 dimension matrix in which 89% of the rows are zero.

One can inspect the DTM, and you might want to do so for fun. However, it isn’t particularly illuminating because of the sheer volume of information that will flash up on the console. To limit the information displayed, one can inspect a small section of it like so:

inspect(dtm[1:2,1000:1005])
<<DocumentTermMatrix (documents: 2, terms: 6)>>
Non-/sparse entries: 0/12
Sparsity : 100%
Maximal term length: 8
Weighting : term frequency (tf)
Docs                               creation creativ credibl credit crimin crinkl
BeyondEntitiesAndRelationships.txt        0        0      0      0      0      0
bigdata.txt                               0        0      0      0      0      0

This command displays terms 1000 through 1005 in the first two rows of the DTM. Note that your results may differ.

Mining the corpus

Notice that in constructing the TDM, we have converted a corpus of text into a mathematical object that can be analysed using quantitative techniques of matrix algebra.  It should be no surprise, therefore, that the TDM (or DTM) is the starting point for quantitative text analysis.

For example, to get the frequency of occurrence of each word in the corpus, we simply sum over all rows to give column sums:

freq <- colSums(as.matrix(dtm))

 

Here we have  first converted the TDM into a mathematical matrix using the as.matrix() function. We have then summed over all rows to give us the totals for each column (term). The result is stored in the (column matrix) variable freq.

Check that the dimension of freq equals the number of terms:

#length should be total number of terms
length(freq)
[1] 4209

 

Next, we sort freq in descending order of term count:

#create sort order (descending)
ord <- order(freq,decreasing=TRUE)

 

Then list the most and least frequently occurring terms:

#inspect most frequently occurring terms
freq[head(ord)]
one organ can manag work system
314 268    244  222   202   193
#inspect least frequently occurring terms
freq[tail(ord)]   
yield yorkshir   youtub     zeno     zero    zulli
1        1        1        1        1        1

 

The  least frequent terms can be more interesting than one might think. This is  because terms that occur rarely are likely to be more descriptive of specific documents. Indeed, I can recall the posts in which I have referred to Yorkshire, Zeno’s Paradox and  Mr. Lou Zulli without having to go back to the corpus, but I’d have a hard time enumerating the posts in which I’ve used the word system.

There are at least a couple of ways to simple ways to strike a balance between frequency and specificity. One way is to use so-called  inverse document frequencies. A simpler approach is to  eliminate words that occur in a large fraction of corpus documents.   The latter addresses another issue that is evident in the above. We deal with this now.

Words like “can” and “one”  give us no information about the subject matter of the documents in which they occur. They can therefore be eliminated without loss. Indeed, they ought to have been eliminated by the stopword removal we did earlier. However, since such words occur very frequently – virtually in all documents – we can remove them by enforcing bounds when creating the DTM, like so:

dtmr <-DocumentTermMatrix(docs, control=list(wordLengths=c(4, 20),
bounds = list(global = c(3,27))))

 

Here we have told R to include only those words that occur in  3 to 27 documents. We have also enforced  lower and upper limit to length of the words included (between 4 and 20 characters).

Inspecting the new DTM:

dtmr
<<DocumentTermMatrix (documents: 30, terms: 1290)>>
Non-/sparse entries: 10002/28698
Sparsity : 74%
Maximal term length: 15
Weighting : term frequency (tf)

The dimension is reduced to 30 x 1290.

Let’s calculate the cumulative frequencies of words across documents and sort as before:

reqr <- colSums(as.matrix(dtmr))
#length should be total number of terms
length(freqr)
[1] 1290
#create sort order (asc)
ordr <- order(freqr,decreasing=TRUE)
#inspect most frequently occurring terms
freqr[head(ordr)]
organ manag work system project problem
268     222  202    193     184     171
#inspect least frequently occurring terms
freqr[tail(ordr)]
wait warehous welcom whiteboard wider widespread
3           3      3          3     3          3

 

The results make sense: the top 6 keywords are pretty good descriptors of what my blogs is about – projects, management and systems. However, not all high frequency words need be significant. What they do, is give you an idea of potential classification terms.

That done, let’s take get a list of terms that occur at least a  100 times in the entire corpus. This is easily done using the findFreqTerms() function as follows:

findFreqTerms(dtmr,lowfreq=80)
[1] “action” “approach” “base” “busi” “chang” “consult” “data” “decis” “design”
[10] “develop” “differ” “discuss” “enterpris” “exampl” “group” “howev” “import” “issu”
[19] “like” “make” “manag” “mani” “model” “often” “organ” “peopl” “point”
[28] “practic” “problem” “process” “project” “question” “said” “system” “thing” “think”
[37] “time” “understand” “view” “well” “will” “work”

 

Here I have asked findFreqTerms() to return all terms that occur more than 80 times in the entire corpus. Note, however, that the result is ordered alphabetically, not by frequency.

Now that we have the most frequently occurring terms in hand, we can check for correlations between some of these and other terms that occur in the corpus.  In this context, correlation is a quantitative measure of the co-occurrence of words in multiple documents.

The tm package provides the findAssocs() function to do this.  One needs to specify the DTM, the term of interest and the correlation limit. The latter is a number between 0 and 1 that serves as a lower bound for  the strength of correlation between the  search and result terms. For example, if the correlation limit is 1, findAssocs() will return only  those words that always co-occur with the search term. A correlation limit of 0.5 will return terms that have a search term co-occurrence of at least  50% and so on.

Here are the results of  running findAssocs() on some of the frequently occurring terms (system, project, organis) at a correlation of 60%.

 

findAssocs(dtmr,”project”,0.6)
      project
inher 0.82
handl 0.68
manag 0.68
occurr 0.68
manager’ 0.66
findAssocs(dtmr,”enterpris”,0.6)
enterpris
agil       0.80
realist    0.78
increment  0.77
upfront    0.77
technolog  0.70
neither    0.69
solv       0.69
adapt      0.67
architectur 0.67
happi      0.67
movement   0.67
architect  0.66
chanc      0.65
fine       0.64
featur     0.63
findAssocs(dtmr,”system”,0.6)
      system
design  0.78
subset  0.78
adopt   0.77
user    0.77
involv  0.71
specifi 0.71
function 0.70
intend  0.67
softwar 0.67
step    0.67
compos  0.66
intent  0.66
specif  0.66
depart  0.65
phone  0.63
frequent 0.62
today  0.62
pattern 0.61
cognit 0.60
wherea 0.60

 

An important point to note that the presence of a term in these list is not indicative of its frequency.  Rather it is a measure of the frequency with which the two (search and result term)  co-occur (or show up together) in documents across . Note also, that it is not an indicator of nearness or contiguity. Indeed, it cannot be because the document term matrix does not store any information on proximity of terms, it is simply a “bag of words.”

That said, one can already see that the correlations throw up interesting combinations – for example, project and manag, or enterpris and agil or architect/architecture, or system and design or adopt. These give one further insights into potential classifications.

As it turned out,  the very basic techniques listed above were enough for me to get a handle on the original problem that led me to text mining – the analysis of free text problem descriptions in my organisation’s service management tool.  What I did was to work my way through the top 50 terms and find their associations. These revealed a number of sets of keywords that occurred in multiple problem descriptions,  which was good enough for me to define some useful sub-categories.  These are currently being reviewed by the service management team. While they’re busy with that that, I’m looking into refining these further using techniques such as  cluster analysis and tokenization.   A simple case of the latter would be to look at two-word combinations in the text (technically referred to as bigrams). As one might imagine, the dimensionality of the DTM will quickly get out of hand as one considers larger multi-word combinations.

Anyway,  all that and more will topics have to wait for future  articles as this piece is much too long already. That said, there is one thing I absolutely must touch upon before signing off. Do stay, I think you’ll find  it interesting.

Basic graphics

One of the really cool things about R is its graphing capability. I’ll do just a couple of simple examples to give you a flavour of its power and cool factor. There are lots of nice examples on the Web that you can try out for yourself.

Let’s first do a simple frequency histogram. I’ll use the ggplot2 package, written by Hadley Wickham to do this. Here’s the code:

wf=data.frame(term=names(freqr),occurrences=freqr)
library(ggplot2)
p <- ggplot(subset(wf, freqr>100), aes(term, occurrences))
p <- p + geom_bar(stat=”identity”)
p <- p + theme(axis.text.x=element_text(angle=45, hjust=1))
p

 

Figure 1 shows the result.

Fig 1: Term-occurrence histogram (freq>100)

Fig 1: Term-occurrence histogram (freq>100)

The first line creates a data frame – a list of columns of equal length. A data frame also contains the name of the columns – in this case these are term and occurrence respectively.  We then invoke ggplot(), telling it to consider plot only those terms that occur more than 100 times.  The aes option in ggplot describes plot aesthetics – in this case, we use it to specify the x and y axis labels. The stat=”identity” option in geom_bar () ensures  that the height of each bar is proportional to the data value that is mapped to the y-axis  (i.e occurrences). The last line specifies that the x-axis labels should be at a 45 degree angle and should be horizontally justified (see what happens if you leave this out). Check out the voluminous ggplot documentation for more or better yet, this quick introduction to ggplot2 by Edwin Chen.

Finally, let’s create a wordcloud for no other reason than everyone who can seems to be doing it.  The code for this is:

#wordcloud
library(wordcloud)
#setting the same seed each time ensures consistent look across clouds
set.seed(42)
#limit words by specifying min frequency
wordcloud(names(freqr),freqr, min.freq=70)

 

The result is shown Figure 2.

 

Fig 2: Wordcloud (freq>70)

Fig 2: Wordcloud (freq>70)

Here we first load the wordcloud package which is not loaded by default. Setting a seed number ensures that you get the same look each time (try running it without setting a seed). The arguments of the wordcloud() function are straightforward enough. Note that one can specify the maximum number of words to be included instead of the minimum frequency (as I have done above).  See the word cloud  documentation for more.

This word cloud also makes it clear that stop word removal has not done its job well, there are a number of words it has missed (also and however, for example). These can be removed by augmenting the built-in stop word list with a custom one. This is left as an exercise for the reader :-).

Finally, one can make the wordcloud more visually appealing by adding colour as follows:

#…add color
wordcloud(names(freqr),freqr,min.freq=70,colors=brewer.pal(6,”Dark2″))

 

The result is shown Figure 3.

 

 

Fig 3: Wordcloud (freq > 70)

Fig 3: Wordcloud (freq > 70)

You may need to load the RColorBrewer package to get this to work. Check out the brewer documentation to experiment with more colouring options.

Wrapping up

This brings me to the end of this rather long  (but I hope, comprehensible) introduction to text mining R.  It should be clear that despite the length of the article, I’ve covered only the most rudimentary basics.  Nevertheless, I hope I’ve succeeded in conveying  a sense of the possibilities in the vast and rapidly-expanding discipline of text analytics.

 

Note added on July 22nd, 2015:

If you liked this piece, you may want to check out the sequel – a gentle introduction to cluster analysis using R.

Written by K

May 27, 2015 at 8:08 pm

Big Data metaphors we live by

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“When Big Data metaphors erase human sensemaking, and the ways in which values are baked into categories, algorithms and visualizations, we have indeed lost the plot, not found it…” 

Quoted from my essay on metaphors for Big Data, co-written with Simon Buckingham Shum:

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Written by K

May 15, 2015 at 12:12 pm

Posted in Uncategorized

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