Data clustering using bacterial foraging optimization

Authors:
Miao Wan;Lixiang Li;Jinghua Xiao;Cong Wang;Yixian Yang
Affiliations:
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 100876 and Key Laboratory of Network an ...;Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 100876 and Key Laboratory of Network an ...;School of Science, Beijing University of Posts and Telecommunications, Beijing, China 100876;Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 100876 and Key Laboratory of Network an ...;Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China 100876 and Key Laboratory of Network an ...
Venue:
Journal of Intelligent Information Systems
Year:
2012

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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.