Particle swarm optimization algorithm and its application to clustering analysis

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Title:	Particle swarm optimization algorithm and its application to clustering analysis
Authors:	Chen, Ching-yi;Ye, Fun
Contributors:	淡江大學電機工程學系
Date:	2004
Issue Date:	2010-04-15 11:05:04 (UTC+8)
Publisher:	N.Y.: IEEE (Institute of Electrical and Electronic Engineers)
Abstract:	Clustering analysis is applied generally to pattern recognition, color quantization and image classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the social-only model and cognition-only model. This method is quite simple and valid, and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.
Relation:	2004 IEEE International Conference on Networking, Sensing and Control, 2, pp.789-794
DOI:	10.1109/ICNSC.2004.1297047
Appears in Collections:	[Graduate Institute & Department of Electrical Engineering] Proceeding

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