An artificial bee colony approach for clustering

Authors:
Changsheng Zhang;Dantong Ouyang;Jiaxu Ning
Affiliations:
College of Information Science & Engineering, Northeastern University, Shenyang 110819, PR China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, PR China;Institute of Grassland Science Northeast Normal University, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Clustering is a popular data analysis and data mining technique. In this paper, an artificial bee colony clustering algorithm is presented to optimally partition N objects into K clusters. The Deb's rules are used to direct the search direction of each candidate. This algorithm has been tested on several well-known real datasets and compared with other popular heuristics algorithm in clustering, such as GA, SA, TS, ACO and the recently proposed K-NM-PSO algorithm. The computational simulations reveal very encouraging results in terms of the quality of solution and the processing time required.