Chaotic particle swarm optimization for data clustering

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.

► An improved technique which combines chaotic map particle swarm optimization with an acceleration strategy.
► Accelerated chaotic particle swarm optimization (ACPSO) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions.
► ACPSO indicate that this method an ideal alternative for solving data clustering problem.
► Chaos can be described as a bounded nonlinear system with deterministic dynamic behavior that has ergodic and stochastic properties.