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2015 , Vol. 20 >Issue 1: 51 - 55

DOI: https://doi.org/10.1007/s12204-015-1587-x

Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm

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  • (1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things, Jiangnan University, Wuxi 214122, Jiangsu, China; 2. Freshwater Fisheries Research Center, Chinese Academy of Fishery Science, Wuxi 214081, Jiangsu, China)

Online published: 2015-03-10

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Abstract

For the question that fuzzy c-means (FCM) clustering algorithm has the disadvantages of being too sensitive to the initial cluster centers and easily trapped in local optima, this paper introduces a new metric norm in FCM and particle swarm optimization (PSO) clustering algorithm, and proposes a parallel optimization algorithm using an improved fuzzy c-means method combined with particle swarm optimization (AF-APSO). The experiment shows that the AF-APSO can avoid local optima, and get the best fitness and clustering performance significantly.

Key words： fuzzy c-means (FCM); particle swarm optimization (PSO); clustering algorithm; new metric norm

Cite this article

MAO Li1 (毛力), SONG Yi-chun1* (宋益春), LI Yin1 (李引),YANG Hong2 (杨弘), XIAO Wei2 (肖炜) . Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm[J]. Journal of Shanghai Jiaotong University(Science), 2015 , 20(1) : 51 -55 . DOI: 10.1007/s12204-015-1587-x

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