A Hybrid Monkey Search Algorithm for Clustering Analysis. (4th March 2014)

• Record Type: Journal Article
• Authors: Chen, Xin; Zhou, Yongquan; Luo, Qifang
• Other Names: Lopez-Nores M. Academic Editor.; Lou D.-C. Academic Editor.; Martínez L. Academic Editor.; Wu D. Academic Editor.; Xiao L. Academic Editor.
• Is Part Of: TheScientificWorldjournal. Volume 2014(2014)
• Publication Date: 2014-03-04
• Subjects: Science -- Periodicals; Technology -- Periodicals; Medicine -- Periodicals; 505
• DOI: 10.1155/2014/938239
• Languages: English
• ISSNs: 2356-6140

Abstract

Abstract : Clustering is a popular data analysis and data mining technique. The k -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 k -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.