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

Publication series

NameInternational Symposium on Wireless Personal Multimedia Communications (WPMC)
PublisherIEEE
ISSN (Print)1347-6890
ISSN (Electronic)1882-5621

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). (International Symposium on Wireless Personal Multimedia Communications (WPMC)). 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 (International Symposium on Wireless Personal Multimedia Communications (WPMC)).
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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, International Symposium on Wireless Personal Multimedia Communications (WPMC), 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 (International Symposium on Wireless Personal Multimedia Communications (WPMC)).

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. (International Symposium on Wireless Personal Multimedia Communications (WPMC)).