Correlative analysis of soft clustering algorithms

Clustering is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering can be considered one of the most important unsupervised learning techniques so as every other problem of this kind; it deals with finding a structure in a collection of unlabelled data. Clustering is of soft and hard clustering. Hard clustering refers to basic partitioning algorithms where object belongs to only one cluster. Soft clustering refers to data objects belonging to more than one cluster based on its membership values. This paper reviews three types of Soft clustering techniques: Fuzzy C-Mean, Rough C-Mean, and Rough Fuzzy C-Mean. Thereby calculating cluster validity indices for a synthetic dataset and a real dataset on applying these algorithms and ensuring best soft clustering algorithm through experimental analysis.