9 January 2008 Using ant colony optimization for efficient clustering
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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.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yong Wang, Yong Wang, Wei Zhang, Wei Zhang, Jun Chen, Jun Chen, Jianfu Li, Jianfu Li, Li Xiao, Li Xiao, } "Using ant colony optimization for efficient clustering", Proc. SPIE 6794, ICMIT 2007: Mechatronics, MEMS, and Smart Materials, 67944C (9 January 2008); doi: 10.1117/12.784045; https://doi.org/10.1117/12.784045
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