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A new cluster validity index for fuzzy clustering
Sreeram Joopudi, Suraj S. Rathi, S.Narasimhan,
Published in IFAC Secretariat
Volume: 46
Issue: 31
Pages: 325 - 330
Performance of any clustering algorithm depends critically on the number of clusters that are initialized. A practitioner might not know, a priori, the number of partitions into which his data should be divided; to address this issue many cluster validity indices have been proposed for finding the optimal number of partitions. In this paper, we propose a new "Graded Distance index" (GD-index) for computing optimal number of fuzzy clusters for a given data set. The efficiency of this index is compared with well-known existing indices and tested on several data sets. It is observed that the "GD-index" is able to correctly compute the optimal number of partitions in most of the data sets that are tested. © IFAC.
About the journal
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
PublisherIFAC Secretariat
Open AccessNo
Concepts (10)
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    Clustering algorithms
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    Fuzzy clustering
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    Data set
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    Fuzzy c-mean clustering
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    Number of clusters
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    Optimal number
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    Process control