上海交通大学学报(英文版) ›› 2015, Vol. 20 ›› Issue (1): 51-55.doi: 10.1007/s12204-015-1587-x

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

MAO Li1 (毛力), SONG Yi-chun1* (宋益春), LI Yin1 (李引),YANG Hong2 (杨弘), XIAO Wei2 (肖炜)   

  1. (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)
  • 出版日期:2015-02-28 发布日期:2015-03-10
  • 通讯作者: SONG Yi-chun (宋益春) E-mail:yqyls@sina.com

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

MAO Li1 (毛力), SONG Yi-chun1* (宋益春), LI Yin1 (李引),YANG Hong2 (杨弘), XIAO Wei2 (肖炜)   

  1. (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:2015-02-28 Published:2015-03-10
  • Contact: SONG Yi-chun (宋益春) E-mail:yqyls@sina.com

摘要:

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.

关键词: fuzzy c-means (FCM), particle swarm optimization (PSO), clustering algorithm , new metric norm

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

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