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 pap...

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Publicado en:Scientific World Journal pp. 938239 - 938240
Autores principales: Chen, Xin, Zhou, Yongquan, Luo, Qifang
Formato: research Journal Article
Publicado: Wiley-Blackwell 2014
Acceso en línea:Ver este registro en EBSCOhost
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        atl: A hybrid monkey search algorithm for clustering analysis.
      aug:
        au:
          Chen, Xin
          Zhou, Yongquan
          Luo, Qifang
        affil: College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China.
      sug:
        subj:
          Algorithms
          Artificial Intelligence
          Cluster Analysis
          Data Mining Methods
          Computer Simulation
          Primates Physiology
          Reproducibility of Results
          Animal Studies
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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