Cluster analysis for overlapping clusters using genetic algorithm

Cluster analysis is an important task almost in all fields including bioinformatics, social networks, agriculture, and so on. It basically explores the natural structure of the data without any prior knowledge about it. In many real data sets, the objects reside in many clusters with different membership values. Many clustering algorithms have been proposed for finding such overlapping clusters to analyze high volume of data. In the paper, genetic algorithm based cluster analysis technique is proposed for finding the optimal set of overlapping clusters. The usefulness of applying the genetic algorithm based optimization technique is to assign a membership value only to the objects which are the members of several clusters, instead of assigning membership values for all clusters like fuzzy clustering algorithm. If any object positively belongs to a cluster, its membership value for this cluster is `1' and `0' for all other clusters. The overall performance of the method is investigated on some popular UCI data sets and the optimality of the clusters is measured by related cluster validation indices. The experimental results show the effectiveness of the proposed method.