A cluster validity index based on frequent pattern

Hongyan Cui, Kuo Zhang, Xu Huang, Yunjie Liu

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    1 Citation (Scopus)

    Abstract

    Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices
    Original languageEnglish
    Title of host publicationInternational Symposium on Wireless Personal Multimedia Communications, WPMC
    EditorsAshutosh Dutta
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages1-6
    Number of pages6
    Volume1
    Publication statusPublished - 2013
    Event16th International Symposium on Wireless Personal Multimedia Communications, 2013 - Atlantic City, Atlantic City, United States
    Duration: 24 Jun 201327 Jun 2013

    Conference

    Conference16th International Symposium on Wireless Personal Multimedia Communications, 2013
    CountryUnited States
    CityAtlantic City
    Period24/06/1327/06/13

    Fingerprint

    Clustering algorithms
    Fuzzy clustering
    Derivatives

    Cite this

    Cui, H., Zhang, K., Huang, X., & Liu, Y. (2013). A cluster validity index based on frequent pattern. In A. Dutta (Ed.), International Symposium on Wireless Personal Multimedia Communications, WPMC (Vol. 1, pp. 1-6). USA: IEEE, Institute of Electrical and Electronics Engineers.
    Cui, Hongyan ; Zhang, Kuo ; Huang, Xu ; Liu, Yunjie. / A cluster validity index based on frequent pattern. International Symposium on Wireless Personal Multimedia Communications, WPMC. editor / Ashutosh Dutta. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1-6
    @inproceedings{d979cbc99f144b6b932ba4176ce6dc56,
    title = "A cluster validity index based on frequent pattern",
    abstract = "Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices",
    keywords = "cluster, index, frequent pattern",
    author = "Hongyan Cui and Kuo Zhang and Xu Huang and Yunjie Liu",
    year = "2013",
    language = "English",
    volume = "1",
    pages = "1--6",
    editor = "Ashutosh Dutta",
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    Cui, H, Zhang, K, Huang, X & Liu, Y 2013, A cluster validity index based on frequent pattern. in A Dutta (ed.), International Symposium on Wireless Personal Multimedia Communications, WPMC. vol. 1, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1-6, 16th International Symposium on Wireless Personal Multimedia Communications, 2013, Atlantic City, United States, 24/06/13.

    A cluster validity index based on frequent pattern. / Cui, Hongyan; Zhang, Kuo; Huang, Xu; Liu, Yunjie.

    International Symposium on Wireless Personal Multimedia Communications, WPMC. ed. / Ashutosh Dutta. Vol. 1 USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1-6.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    N2 - Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices

    AB - Since a clustering algorithm can produce as many partitions as desired, one need to assess their quality in order to select the partition that most represents the structure in the data. This is the rationale for the cluster-validity (CV) problem and indices. This paper proposes a CV index for fuzzy-clustering algorithm, such as the fuzzy c-means (FCM) or its derivatives. Given a fuzzy partition, this new index uses global information and is based on more logical reasoning than geometrical features. Experimental results on artificial and benchmark datasets are given to demonstrate the performance of the proposed index, as compared with traditional and recent indices

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    Cui H, Zhang K, Huang X, Liu Y. A cluster validity index based on frequent pattern. In Dutta A, editor, International Symposium on Wireless Personal Multimedia Communications, WPMC. Vol. 1. USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1-6