Abstract—Clustering is a popular data analysis and data mining technique. Among different proposed methods,
k -means is an efficient clustering technique to cluster datasets, but this method highly depends on the initial state and usually converges to local optimum solution. This paper takes the advantage
of a novel evolutionary algorithm, called artificial bee colony (ABC), to improve the capability of
k -means in finding global optimum clusters in nonlinear partitional clustering problems. The proposed
method is the combination of
k -means and ABC algorithms, called kABC, which can find better cl
uster portions. Both kABC and
k-means are run on three known
data sets from the UCI Machine Learning Repository. The simulation results show that the combination of ABC and
k-means technique has more ability to search for global optimum solutions and more ability for passing local optimum.
Index Terms—Artificial bee colony algorithm,
k-means.
The authors are with IASC (Intelligent Agents and Soft-Computing) group, Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d'Armi, I-09123, Cagliari, Italy (e-mail: armano@daiee.unica.it, mohammad.farmani@daiee.unica.it).
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Cite:Giuliano Armano and Mohammad Reza Farmani, "Clustering Analysis with Combination of Artificial Bee Colony Algorithm and k-Means Technique," International Journal of Computer Theory and Engineering vol. 6, no. 2, pp. 141-145, 2014.
