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Proceedings Paper

Using ant colony optimization for efficient clustering
Author(s): Yong Wang; Wei Zhang; Jun Chen; Jianfu Li; Li Xiao
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Paper Abstract

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.

Paper Details

Date Published: 9 January 2008
PDF: 5 pages
Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67944C (9 January 2008); doi: 10.1117/12.784045
Show Author Affiliations
Yong Wang, Chongqing Education College (China)
Wei Zhang, Chongqing Education College (China)
Jun Chen, Chongqing Education College (China)
Jianfu Li, Chongqing Education College (China)
Li Xiao, Chongqing Education College (China)


Published in SPIE Proceedings Vol. 6794:
ICMIT 2007: Mechatronics, MEMS, and Smart Materials
Minoru Sasaki; Gisang Choi Sang; Zushu Li; Ryojun Ikeura; Hyungki Kim; Fangzheng Xue, Editor(s)

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