A Hybrid Approach for Data Clustering using Expectation-Maximization and Parameter Adaptive Harmony Search Algorithm

Abstract

This paper presents a novel hybrid data clustering algorithm based on parameter adaptive harmony search algorithm. The recently developed parameter adaptive harmony search algorithm (PAHS) is used to refine the cluster centers, which are further used in initializing Expectation-Maximization clustering algorithm. The optimal number of clusters are determined through four well-known cluster validity indices. The proposed algorithm is evaluated on three real life datasets and compared with the performance of K-Means, Fuzzy CMeans and HS initialize EM (HSEM). Experimental results reveal that the proposed approach provide better results in terms of precision, recall, weighted average, F-Measure and G-Measure.