Using ant colony optimization for efficient clustering

被引：0

作者：

Yong Wang ^{[1
]}

Wei Zhang ^{[1
]}

Jun Chen ^{[1
]}

Jianfu Li ^{[1
]}

Li Xiao ^{[1
]}

机构：

[1] Chongquing Educ Coll, Dept Comp, Chongqing 400067, Peoples R China

来源：

ICMIT 2007: MECHATRONICS, MEMS, AND SMART MATERIALS, PTS 1 AND 2 | 2008年 / 6794卷

关键词：

ACO; data mining; clustering;

D O I：

10.1117/12.784045

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

To improve the performance of data clustering, this study proposes a novel clustering method called ABCA (ACO Based Clustering Algorithm). The presented method is based on heuristic concept and using Ant Colony Optimization algorithm (ACO) to obtain global search. The main advantage of these algorithms lies in the fact that no additional information, such as an initial partitioning of the data or the number of clusters, is needed. Since the proposed method is very efficiently, thus it can perform data clustering very quickly.

引用

页数：5

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