Fuzzy C-Mean Clustering Algorithms Based on Picard Iteration and Particle Swarm Optimization

被引：4

作者：

Liu, Hsiang-Chuan ^{[1
]}

Yih, Jeng-Ming ^{[2
,3
]}

Wu, Der-Bang ^{[2
,3
]}

Liu, Shin-Wu ^{[4
]}

机构：

[1] Asia Univ, Dept Bioinformat, Taichung 41354, Taiwan

[2] Taichung Univ, Grad Inst Educ Measurement, Taichung 40306, Taiwan

[3] Taichung Univ, Dept Math Educ, Taichung 40306, Taiwan

[4] Rutgers State Univ, Dept Cell & Dev Biol, New Brunswick, NJ 08903 USA

来源：

2008 INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND TRAINING AND 2008 INTERNATIONAL WORKSHOP ON GEOSCIENCE AND REMOTE SENSING, VOL 2, PROCEEDINGS, | 2009年

关键词：

D O I：

10.1109/ETTandGRS.2008.375

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, "Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)", is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.

引用

页码：838 / +

页数：2

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