基于粒子群优化的直觉模糊核聚类算法研究

doi:10.11959/j.issn.1000-436x.2015099

Abstract

Abstract:

The intuitionistic fuzzy kernel c-means clustering algorithm has several problems such as sensitivity to the ini-tial value,low convergence speed,etc.To overcome these shortages,the particle swarm optimization (PSO) algorithm with powerful ability of global search and quick convergence rate is applied to intuitionistic fuzzy clustering.Firstly,PSO is used to optimize the initial clustering centers.Then,the approach of intuitionistic fuzzy kernel clustering based on PSO,namely PS-IFKCM,is proposed.This algorithm can enhance both the clustering ability and the convergence speed.Fi-nally,experiments based on four measured datasets are carried out to illustrate the performance of the proposed method.Compared with results from FCM and IFKCM,PS-IFKCM is of great efficiency for classification.

Key words: intuitionistic fuzzy sets, kernel method, fuzzy clustering, particle swarm optimization

UXiao-dong Y,EIYing-jie L,UEShao-hua Y,ANGRui W. Research on PSO-based intuitionistic fuzzy kernel clustering algorithm[J]. Journal on Communications, 2015, 36(5): 74-80.

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References 25

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数据集	FCM	IFKCM	PS-IFKCM
Iris	92.76	94.74	96.91
Wine	92.36	95.37	96.73
Wisc	93.58	95.46	96.89
Motorcycle	93.26	94.88	97.07

数据集	FCM	IFKCM	PS-IFKCM
Iris	0.106	6.256	2.025
Wine	0.289	9.134	3.131
Wisc	0.365	9.789	3.541
Motorcycle	0101	4.378	1.416

Research on PSO-based intuitionistic fuzzy kernel clustering algorithm

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