Distance based hybrid approach for cluster analysis using variants of K-means and evolutionary algorithm

Abstract

Clustering is a process of grouping same objects into a specified number of clusters. K-means and K-medoids algorithms are the most popular partitional clustering techniques for large data sets. However, they are sensitive to random selection of initial centroids and are fall into local optimal solution. K-means++ algorithm has good convergence rate than other algorithms. Distance metric is used to find the dissimilarity between objects. Euclidean distance metric is commonly used by number of researchers in most algorithms. In recent years, Evolutionary algorithms are the global optimization techniques for solving clustering problems. In this study, we present hybrid K-means++ with PSO technique (K++_PSO) clustering algorithm based on different distance metrics like City Block and Chebyshev. The algorithms are tested on four popular benchmark data sets from UCI machine learning repository and an artificial data set. The clustering results are evaluated through the fitness function values. We have made a comparative study of proposed algorithm with other algorithms. It has been found that K++_PSO algorithm using Chebyshev distance metric produces good clustering results as compared to other approaches.