Accelerated Linearly Decreasing Weight Particle Swarm Optimization for Data Clustering
Cheng‐Hong Yang, Chih-Jen Hsiao, Li‐Yeh Chuang · 2010
Data clustering is a powerful technique designed specifically for discerning the structure of and simplifying the complexity of large scale data. It is a technique commonly used for statistical data analysis, and is also used in many other fields, including machine learning, data mining, pattern recognition, image analysis, and bioinformatics, in which the distribution of information can be of any size and shape. An improved technique combining linearly decreasing weight particle swarm optimization (LDWPSO) with an acceleration strategy is proposed in this paper. Accelerated linearly decreasing weight particle swarm optimization (ALDWPSO) searches for cluster centers in an arbitrary data set and can effectively indentify the global optima. ALDWPSO is tested on six experimental data sets, and its performance is compared to the performance of PSO, NM-PSO, K-PSO, K-NM-PSO, LDWPSO and K-means clustering. Results indicate that ALDWPSO is both robust and suitable for solving data clustering problem.