Data clustering using hybrid particle swarm optimization

Authors:
Ahmed A. A. Esmin;Stan Matwin
Affiliations:
Department of Computer Science, University of Lavras (UFLA), Lavras, MG, Brazil,School of Electrical Engineering and Computer Science, University of Ottawa (uOttawa), Ottawa, ON, Canada;School of Electrical Engineering and Computer Science, University of Ottawa (uOttawa), Ottawa, ON, Canada,Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
Venue:
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Year:
2012

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Abstract

Clustering is an important data mining task and has been explored extensively by a number of researchers for different application areas, such as text application and bioinformatics data. In this paper we propose the use of a novel algorithm for clustering data that we call hybrid particle swarm optimization with mutation (HPSOM), which is based on PSO. The HPSOM basically uses PSO and incorporates the mutation process often used in GA to allow the search to escape from local optima. It is shown how the PSO/HPSOM can be used to find the centroids of a user-specified number of clusters. The new algorithm is evaluated on five benchmark data sets. The proposed method is compared with the K-means (KM) clustering technique and the standard PSO algorithm. The results show that the algorithm is efficient and produces compact clusters.