Bezdek type fuzzy attribute C-means clustering algorithm

Jingwei Liu; Meizhi Xu

Bezdek type fuzzy attribute C-means clustering algorithm

Jingwei Liu^*
, Meizhi Xu

^*Corresponding author for this work

School of Mathematical Sciences

Tsinghua University

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Bezdek type fuzzy attribute C-means clustering algorithm (FAMC) was proposed by extending attribute means clustering (AMC) algorithm based on fuzziness index (or weighting exponent) m. The iterative algorithm was derived and the effect of fuzziness index m on objective function convergence was discussed. The experimental results of pattern recognition performances on standard Iris database and tumor/normal gene chip expression data demonstrate that FAMC is more effective than fuzzy C-means clustering (FCM) algorithm and AMC.

Original language	English
Pages (from-to)	1121-1126
Number of pages	6
Journal	Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
Volume	33
Issue number	9
State	Published - Sep 2007

Keywords

Attribute means clustering
Fuzzy C-means clustering
Gene expression data
Stable function

Cite this

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title = "Bezdek type fuzzy attribute C-means clustering algorithm",

abstract = "Bezdek type fuzzy attribute C-means clustering algorithm (FAMC) was proposed by extending attribute means clustering (AMC) algorithm based on fuzziness index (or weighting exponent) m. The iterative algorithm was derived and the effect of fuzziness index m on objective function convergence was discussed. The experimental results of pattern recognition performances on standard Iris database and tumor/normal gene chip expression data demonstrate that FAMC is more effective than fuzzy C-means clustering (FCM) algorithm and AMC.",

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author = "Jingwei Liu and Meizhi Xu",

year = "2007",

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AU - Liu, Jingwei

AU - Xu, Meizhi

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AB - Bezdek type fuzzy attribute C-means clustering algorithm (FAMC) was proposed by extending attribute means clustering (AMC) algorithm based on fuzziness index (or weighting exponent) m. The iterative algorithm was derived and the effect of fuzziness index m on objective function convergence was discussed. The experimental results of pattern recognition performances on standard Iris database and tumor/normal gene chip expression data demonstrate that FAMC is more effective than fuzzy C-means clustering (FCM) algorithm and AMC.

KW - Attribute means clustering

KW - Fuzzy C-means clustering

KW - Gene expression data

KW - Stable function

UR - https://www.scopus.com/pages/publications/35949003613

M3 - 文章

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SN - 1001-5965

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SP - 1121

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JO - Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics

JF - Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics

IS - 9

ER -

Bezdek type fuzzy attribute C-means clustering algorithm

Abstract

Keywords

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