Two-Stage Clustering Based on Cluster Validity Measures

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Abstract

<p>To handle a large-scale object, a two-stage clustering method has been previously proposed. The method generates a large number of clusters during the first stage and merges clusters during the second stage. In this paper, a novel two-stage clustering method is proposed by introducing cluster validity measures as the merging criterion during the second stage. The significant cluster validity measures used to evaluate cluster partitions and determine the suitable number of clusters act as the criteria for merging clusters. The performance of the proposed method based on six typical indices is compared with eight artificial datasets. These experiments show that a trace of the fuzzy covariance matrix W<sub>tr</sub> and its kernelization KW<sub>tr</sub> are quite effective when applying the proposed method, and obtain better results than the other indices.</p>

Journal

  • Journal of Advanced Computational Intelligence and Intelligent Informatics

    Journal of Advanced Computational Intelligence and Intelligent Informatics 22(1), 54-61, 2018

    Fuji Technology Press Ltd.

Codes

  • NII Article ID (NAID)
    130007492659
  • NII NACSIS-CAT ID (NCID)
    AA12042502
  • Text Lang
    ENG
  • ISSN
    1343-0130
  • NDL Article ID
    028764951
  • NDL Call No.
    Z78-A599
  • Data Source
    NDL  J-STAGE 
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