Clustering Quality Improvement of K-Means Using a Hybrid Evolutionary Model

Abstract

Choosing good candidates for the initial centroid selection process for compact clustering algorithms, such as k-means, is essential for clustering quality and performance. In this study, a novel hybrid evolutionary model for k-means clustering (HE-kmeans) is proposed. This model uses meta-heuristic methods to identify the "good candidates" for initial centroid selection in k-means clustering method. The results indicate that the clustering quality is improved by approximately 30% compared to the standard random selection of initial centroids. We also experimentally compare our method with the other heuristics proposed for initial centroid selection and the experimental results show that our method performs better in most cases. (C) 2015 The Authors. Published by Elsevier B.V.