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Particle swarm optimization methods for data clustering

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2 Author(s)
Johnson, R.K. ; Rochester Inst. of Technol., Rochester, NY, USA ; Sahin, F.

This paper discusses the application of particle swarm optimization (PSO) to data clustering. Four different methods of PSO are tested on six test data sets and compared to k-means and fuzzy c-means. The four PSO methods, combinations of the constriction method, inertia, and the predator-prey method all out-perform k-means and fuzzy c-means in all test cases to varying degrees in terms of quantization error.

Published in:

Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on

Date of Conference:

2-4 Sept. 2009