Clustering techniques

In this section, we will discuss clustering techniques along with challenges and suitable examples. A brief overview of hierarchical clustering, centroid-based clustering, and distribution-based clustering will be provided too.

Unsupervised learning and the clustering

Clustering analysis is about dividing data samples or data points and putting them into corresponding homogeneous classes or clusters. Thus a trivial definition of clustering can be thought as the process of organizing objects into groups whose members are similar in some way.
A cluster is, therefore, a collection of objects that are similar between them and are dissimilar to the objects belonging to other clusters. As shown in Figure 2...