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Fuzzy clustering with volume prototypes and adaptive cluster merging

Kaymak, U and Setnes, M 2002, 'Fuzzy clustering with volume prototypes and adaptive cluster merging' , IEEE Transactions on Fuzzy Systems, 10 (6) , pp. 705-712.

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    Abstract

    Two extensions to the objective function-based fuzzy clustering are proposed. First, the (point) prototypes are extended to hypervolumes, whose size can be fixed or can be determined automatically from the data being clustered. It is shown that clustering with hypervolume prototypes can be formulated as the minimization of an objective function. Second, a heuristic cluster merging step is introduced where the similarity among the clusters is assessed during optimization. Starting with an overestimation of the number of clusters in the data, similar clusters are merged in order to obtain a suitable partitioning. An adaptive threshold for merging is proposed. The extensions proposed are applied to Gustafson–Kessel and fuzzy c-means algorithms, and the resulting extended algorithm is given. The properties of the new algorithm are illustrated by various examples.

    Item Type: Article
    Uncontrolled Keywords: Cluster merging, fuzzy clustering, similarity, volume prototypes
    Themes: Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology
    Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
    Journal or Publication Title: IEEE Transactions on Fuzzy Systems
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Refereed: Yes
    ISSN: 10636706
    Related URLs:
    Depositing User: Institutional Repository
    Date Deposited: 12 Mar 2009 15:34
    Last Modified: 20 Aug 2013 16:56
    URI: http://usir.salford.ac.uk/id/eprint/1819

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