Using ant colony optimization for efficient clustering

Yong Wang; Wei Zhang; Jun Chen; Jianfu Li; Li Xiao

doi:10.1117/12.784045

9 January 2008 Using ant colony optimization for efficient clustering

Yong Wang, Wei Zhang, Jun Chen, Jianfu Li, Li Xiao

Proceedings Volume 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials; 67944C (2008) https://doi.org/10.1117/12.784045
Event: ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 2007, Gifu, Japan

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.

Citation Download Citation

Yong Wang, Wei Zhang, Jun Chen, Jianfu Li, and Li Xiao "Using ant colony optimization for efficient clustering", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67944C (9 January 2008); https://doi.org/10.1117/12.784045

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