A hybrid monkey search algorithm for clustering analysis.
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.
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China.
Xin
X
Chen
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China ; Guangxi Key Laboratory of Hybrid Computation and Integrated Circuit Design Analysis, Nanning Guangxi 530006, China.
Yongquan
Y
Zhou
College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China.
Qifang
Q
Luo
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003198:Computer Simulation; D057225:Data Mining / Q000379:methods*; D000882:Haplorhini / Q000502:physiology; D015203:Reproducibility of Results