A K-harmonic means Clustering Algorithm Based on Enhanced Differential Evolution

被引：8

作者：

Zhang, LiDong ^{[1
,2
]}

Mao, Li ^{[1
,2
]}

Gong, HuaiJin ^{[1
,2
]}

Yang, Hong ^{[3
]}

机构：

[1] Jiangsu Engn R&D Ctr Informat Fus Software, Jiangyin 214405, Jiangsu, Peoples R China

[2] Jiangsu Engn R&D Ctr Informat Fus Software, Jiangyin 214405, Jiangsu, Peoples R China

[3] Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Jiangsu, Peoples R China

来源：

2013 FIFTH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2013) | 2013年

关键词：

K-harmonic means; differential evolution; Laplace mutation operator; logarithmically crossover probability; OPTIMIZATION;

D O I：

10.1109/ICMTMA.2013.1

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The conventional K-harmonic means is tend to be trapped by local optima. To resolve this problem, a novel K-harmonic means clustering algorithm using enhanced differential evolution technique is proposed. This algorithm improves the global search ability by applying Laplace mutation operator and logarithmically crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the K-harmonic means, and improves the global search ability.

引用

页码：13 / 16

页数：4

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