Can classification problems be solved by unsupervised clustering?
By: Tech Evangelist
Tags: Machine Learning, Supervised Learning, Classification, Unsupervised Learning, Artificial Intelligence

Definition of Classification: “The action or process of classifying something according to shared qualities or characteristics.”

“It’s all about finding patterns and understanding what differentiates them.”

The answer to the given question largely depends on what a person understands by the term ‘classification’. One might argue that new data points can only be classified when we have a “knowledge” about previous data points.

However, I would counter that argument by saying that even when we do training for a supervised classification task, we understand the structure of that data. Then, we try to understand the data attributes which differentiates one data point from another. If we select the wrong set of attributes then the training would surely be of no use, and the classifier won’t work on new data points with accuracy.

So, doesn’t that mean that we have the “knowledge” even before the supervised training about the kind of data attributes defining a data point? All we do during the training phase is just to estimate the threshold values for these attributes that differentiate one type of data point from another. The training is often called “parameter estimation”.

Now, what if the estimation of these threshold values for these data attributes be estimated in an unsupervised manner. If these thresholds are estimated, we would know the shared qualities of a particular class or category of the data point, and thus would be able to classify new data points. Hence, solving the classification problem by unsupervised learning.

Ok, but what’s the catch? The catch is that understanding only the important data attributes is difficult. Researchers spend a lot of time in selecting different data attributes and training them and evaluating the model. After a number of iterations, researchers can say that they have found a working model.

An example of this could be classifying movie reviews in positive and negative categories. For example, in the paper “Movie review mining: A comparison between supervised and unsupervised classification approaches. “[1], the authors have identified phrases and POS tags which are the main characteristics of a negative review. Next, they do a semantic-based clustering(not by matching exact words, but checking semantic similarity), and group all the existing reviews in positive and negative classes. Once this unsupervised clustering is done, new movie reviews could be classified by simply using something like K-Nearest Neighbor, with these two groups.

In a second example, the paper “Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity” [2] explains the extraction of named entities from text with unsupervised clustering approach. This is something which is traditionally done with a supervised approach. But, the same output of unsupervised clustering approach as a supervised approach here gives us a hint that identifying classes could be done in an unsupervised manner as well.

Everything explained in this post are completely the author’s thoughts, and may not completely align with what people think in general. I would love to know what other people think about this. Do you agree or disagree?

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