An artificial bee colony approach for clustering

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.