Clustering techniques

In this section, we will discuss clustering techniques along with 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 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, if a collection of objects is given, clustering algorithms put those objects into a group based on similarity. A clustering algorithm such as K-means has then located the centroid of the group of data points. However, to make the clustering accurate and effective, the algorithm evaluates...