A K-means Clustering Algorithm Based on Enhanced Differential Evolution

被引：1
|
作者：
Mao, Li [1 ]
Gong, Huaijin [1 ]
Liu, Xingyang [1 ]
机构：
[1] Jiangnan Univ, Sch Internet Things, Minist Educ, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Jiangsu, Peoples R China
来源：
ADVANCED MANUFACTURING SYSTEMS | 2011年 / 339卷
关键词：
cluster analysis; differential evolution; k-means cluster algorithm; Laplace distribution; exponentially increasing crossover probability; OPTIMIZATION;
D O I：
10.4028/www.scientific.net/AMR.339.71
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local optima. To resolve these problems, a novel k-means clustering algorithm using enhanced differential evolution technique is proposed in this paper. This algorithm improves the global search ability by applying Laplace mutation operator and exponentially increasing crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the conventional k-means algorithms, and improves search ability with higher accuracy, faster convergence speed and better robustness.
引用
收藏
页码：71 / 75
页数：5
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