Abstract
Clustering divides data into meaningful or useful groups (clusters) without any prior knowledge. It is a key technique in data mining and has become an important issue in many fields. This article presents a new clustering algorithm based on the mechanism analysis of Bacterial Foraging (BF). It is an optimization methodology for clustering problem in which a group of bacteria forage to converge to certain positions as final cluster centers by minimizing the fitness function. The quality of this approach is evaluated on several well-known benchmark data sets. Compared with the popular clustering method named k-means algorithm, ACO-based algorithm and the PSO-based clustering technique, experimental results show that the proposed algorithm is an effective clustering technique and can be used to handle data sets with various cluster sizes, densities and multiple dimensions.
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Acknowledgements
I would like to thank the editor and all the reviewers for their great supports to our work. Our study is also supported by the National Basic Research Program of China (973 Program) (2007CB311203), the National Natural Science Foundation of China (Grant No. 60805043, 60821001), the Beijing Natural Science Foundation (Grant No. 4092029), the Huo Ying-Dong Education Foundation of China (Grant No. 121062), and the Foundation for the Author of National Excellent Doctoral Dissertation of PR China (FANEDD) (Grant No. 200951).
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Wan, M., Li, L., Xiao, J. et al. Data clustering using bacterial foraging optimization. J Intell Inf Syst 38, 321–341 (2012). https://doi.org/10.1007/s10844-011-0158-3
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DOI: https://doi.org/10.1007/s10844-011-0158-3