Application of ant K-means on clustering analysis

被引：90

作者：

Kuo, RJ ^{[1
]}

Wang, HS ^{[1
]}

Hu, TL ^{[1
]}

Chou, SH ^{[1
]}

机构：

[1] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei 106, Taiwan

来源：

COMPUTERS & MATHEMATICS WITH APPLICATIONS | 2005年 / 50卷 / 10-12期

关键词：

data mining; clustering analysis; ant colony optimization;

D O I：

10.1016/j.camwa.2005.05.009

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

This paper intends to propose a novel clustering method, ant K-means (AK) algorithm. AK algorithm modifies the K-means as locating the objects in a cluster with the probability, which is updated by the pheromone, while the rule of updating pheromone is according to total within cluster variance (TWCV). The computational results showed that it is better than the other two methods, self-organizing feature map (SOM) followed by K-means method and SOM followed by genetic K-means algorithm via 243 data sets generated by Monte Carlo simulation. To further testify this novel method, the questionnaire survey data for the plasma television market segmentation is employed. The results also indicated that the proposed method is the best among these three methods based on TWCV. (c) 2005 Elsevier Ltd. All rights reserved.

引用

页码：1709 / 1724

页数：16

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