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The Scientific World Journal
Volume 2014, Article ID 938239, 16 pages
http://dx.doi.org/10.1155/2014/938239
Research Article

A Hybrid Monkey Search Algorithm for Clustering Analysis

1College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China
2Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning Guangxi 530006, China

Received 6 November 2013; Accepted 22 January 2014; Published 4 March 2014

Academic Editors: M. Lopez-Nores, D.-C. Lou, L. Martínez, D. Wu, and L. Xiao

Copyright © 2014 Xin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Clustering is a popular data analysis and data mining technique. The -means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the -means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis.