Chaotic particle swarm optimization for data clustering

Authors:
Li-Yeh Chuang;Chih-Jen Hsiao;Cheng-Hong Yang
Affiliations:
Department of Chemical Engineering, I-Shou University, Kaohsiung 80041, Taiwan;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80708, Taiwan;Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 80708, Taiwan and Department of Network Systems, Toko University, Chiayi 61363, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Data clustering is a popular analysis tool for data statistics in several fields, including includes pattern recognition, data mining, machine learning, image analysis and bioinformatics, in which the information to be analyzed can be of any distribution in size and shape. Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data. An improved technique which combines chaotic map particle swarm optimization with an acceleration strategy is proposed, since results of one of the most used clustering algorithm, K-means can be jeopardized by improper choices made in the initializing stage. Accelerated chaotic particle swarm optimization (ACPSO) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions. Comparisons of the clustering performance are obtained from tests conducted on six experimental data sets; the algorithms compared with ACPSO includes PSO, CPSO, K-PSO, NM-PSO, K-NM-PSO and K-means clustering. Results of the robust performance from ACPSO indicate that this method an ideal alternative for solving data clustering problem.