Unsupervised Learning: Clustering

Abstract

Clustering classifies objects into groups based on similarity or distance measure. This is an example of unsupervised learning. The main difference between clustering and classification is that the latter has well-defined target classes. The characteristics of target classes are defined by the training data and the models learned from it. That is why classification is supervised in nature. In contrast, clustering tries to define meaningful classes based on data and its similarity or distance. Figure 4-1 illustrates a document clustering process.