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

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 par...

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Vydáno v:Shanghai jiao tong da xue xue bao Ročník 20; číslo 1; s. 51 - 55
Hlavní autor: 毛力 宋益春 李引 杨弘 肖炜
Médium: Journal Article
Jazyk:angličtina
Vydáno: Heidelberg Shanghai Jiaotong University Press 01.02.2015
Témata:
Algorithms
Architecture
Clustering
Computer Science
Electrical Engineering
Engineering
Fuzzy
Fuzzy logic
Fuzzy set theory
Life Sciences
Materials Science
Norms
Optimization
Swarm intelligence
means (FCM)
clustering algorithm
particle swarm optimization (PSO)
TP 183
fuzzy
new metric norm
ISSN:1007-1172, 1995-8188
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Shrnutí: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 experiment shows that the AF-APSO can avoid local optima, significantly. with particle swarm optimization （AF-APSO）. The and get the best fitness and clustering performance
Bibliografie:31-1943/U
fuzzy c-means （FCM）, particle swarm optimization （PSO）, clustering algorithm, new metric norm
MAO Li, SONG Yi-chun, LI Yin YANG Hong, XIAO Wei （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）
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 experiment shows that the AF-APSO can avoid local optima, significantly. with particle swarm optimization （AF-APSO）. The and get the best fitness and clustering performance
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ISSN:1007-1172
1995-8188
DOI:10.1007/s12204-015-1587-x