[SOUND] This lecture is the first one about the text clustering. In this lecture, we are going to talk about the text clustering. This is a very important technique for doing topic mining and analysis. In particular, in this lecture we're going to start with some basic questions about the clustering. And that is, what is text clustering and why we are interested in text clustering. In the following lectures, we are going to talk about how to do text clustering. How to evaluate the clustering results? So what is text clustering? Well, clustering actually is a very general technique for data mining as you might have learned in some other courses. The idea is to discover natural structures in the data. In another words, we want to group similar objects together. In our case, these objects are of course, text objects. For example, they can be documents, terms, passages, sentences, or websites, and then I'll go group similar text objects together. So let's see an example, well, here you don't really see text objects, but I just used some shapes to denote objects that can be grouped together. Now if I ask you, what are some natural structures or natural groups where you, if you look at it and you might agree that we can group these objects based on chips, or their locations on this two dimensional space. So we got the three clusters in this case. And they may not be so much this agreement about these three clusters but it really depends on the perspective to look at the objects. Maybe some of you have also seen thing in a different way, so we might get different clusters. And you'll see another example about this ambiguity more clearly. But the main point of here is, the problem is actually not so well defined. And the problem lies in how to define similarity. And what do you mean by similar objects? Now this problem has to be clearly defined in order to have a well defined clustering problem. And the problem is in general that any two objects can be similar depending on how you look at them. So for example, this will kept the two words like car and horse. So are the two words similar? Well, it depends on how if we look at the physical properties of car and horse they are very different but if you look at them functionally, a car and a horse, can both be transportation tools. So in that sense, they may be similar. So as we can see, it really depends on our perspective, to look at the objects. And so it ought to make the clustering problem well defined. A user must define the perspective for assessing similarity. And we call this perspective the clustering bias. And when you define a clustering problem, it's important to specify your perspective for similarity or for defining the similarity that will be used to group similar objects. because otherwise, the similarity is not well defined and one can have different ways to group objects. So let's look at the example here. You are seeing some objects, or some shapes, that are very similar to what you have seen on the first slide, but if I ask you to group these objects, again, you might feel there is more than here than on the previous slide. For example, you might think, well, we can steer a group by ships, so that would give us cluster that looks like this. However, you might also feel that, well, maybe the objects can be grouped based on their sizes. So that would give us a different way to cluster the data if we look at the size and look at the similarity in size. So as you can see clearly here, depending on the perspective, we'll get different clustering result. So that also clearly tells us that in order to evaluate the clustering without, we must use perspective. Without perspective, it's very hard to define what is the best clustering result. So there are many examples of text clustering setup. And so for example, we can cluster documents in the whole text collection. So in this case, documents are the units to be clustered. We may be able to cluster terms. In this case, terms are objects. And a cluster of terms can be used to define concept, or theme, or a topic. In fact, there's a topic models that you have seen some previous lectures can give you cluster of terms in some sense if you take terms with high probabilities from word distribution. Another example is just to cluster any text segments, for example, passages, sentences, or any segments that you can extract the former larger text objects. For example, we might extract the order text segments about the topic, let's say, by using a topic model. Now once we've got those text objects then we can cluster the segments that we've got to discover interesting clusters that might also ripple in the subtopics. So this is a case of combining text clustering with some other techniques. And in general you will see a lot of text mining can be accurate combined in a flexible way to achieve the goal of doing more sophisticated mining and analysis of text data. We can also cluster fairly large text objects and by that, I just mean text objects may contain a lot of documents. So for example, we might cluster websites. Each website is actually compose of multiple documents. Similarly, we can also cluster articles written by the same author, for example. So we can trigger all the articles published by also as one unit for clustering. In this way, we might group authors together based on whether they're published papers or similar. For the more text clusters will be for the cluster to generate a hierarchy. That's because we can in general cluster any text object at different levels. So more generally why is text clustering interesting? Well, it's because it's a very useful technique for text mining, particularly exploratory text analysis. And so a typical scenario is that you were getting a lot of text data, let's say all the email messages from customers in some time period, all the literature articles, etc. And then you hope to get a sense about what are the overall content of the connection, so for example, you might be interested in getting a sense about major topics, or what are some typical or representative documents in the connection. And clustering to help us achieve this goal. We sometimes also want to link a similar text objects together. And these objects might be duplicated content, for example. And in that case, such a technique can help us remove redundancy and remove duplicate documents. Sometimes they are about the same topic and by linking them together we can have more complete coverage of a topic. We may also used text clustering to create a structure on the text data and sometimes we can create a hierarchy of structures and this is very useful for problems. We may also use text clustering to induce additional features to represent text data when we cluster documents together, we can treat each cluster as a feature. And then we can say when a document is in this cluster and then the feature value would be one. And if a document is not in this cluster, then the feature value is zero. And this helps provide additional discrimination that might be used for text classification as we will discuss later. So there are, in general, many applications of text clustering. And I just thought of two very specific ones. One is to cluster search results, for example, [INAUDIBLE] search engine can cluster such results so that the user can see overall structure of the results of return the fall query. And when the query's ambiguous this is particularly useful because the clusters likely represent different senses of ambiguous word. Another application is to understand the major complaints from customers based on their emails, right. So in this case, we can cluster email messages and then find in the major clusters from there, we can understand what are the major complaints about them. [MUSIC]
