Empirical analysis of K-means, fuzzy C-means and particle swarm optimization for data clustering

Clustering is a fundamental task in data mining technique which puts more similar data objects into one group and dissimilar objects into another group. The aim of this paper is to compare the quality of clusters produced by K-Means, Particle swarm optimization (PSO) and Fuzzy C-Means (FCM) for data clustering. The k-means algorithm is the most widely used partitional clustering algorithm technique in the industries and academia. The algorithm is simple and easy to implement. The main drawback of the K-Means algorithm is that it is sensitive to the selection of the initial cluster centers and it may converge to local optima. Fuzzy C-means algorithm is a popular algorithm in the field of fuzzy clustering. Fuzzy clustering using FCM can provide a data partition that is both better and more meaningful than hard clustering approaches. Particle Swarm Optimization (PSO) is an evolutionary computational technique which was motivated by the organism’s behavior such as schooling of fish and flocking of birds. The quality of the clusters produced by above three algorithms is estimated using Silhouette Coefficient. The experimental results show that the performance of PSO clustering is better than FCM & K-Means clustering. The difference in time taken by the algorithms for execution is negligible.