Fuzzy Entropy Clustering Using Possibilistic Approach

Fuzzy entropy clustering (FEC) is sensitive to noises the same as fuzzy c-means (FCM) clustering because the probabilistic constraints in their memberships. To solve this noise sensitive problem of FCM, Krishnapuram and Keller have presented the possibilistic c-means (PCM) clustering by abandoning t...

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Bibliographic Details
Main Authors: Hai-Jun, Fu, Xiao-Hong, Wu, Han-Ping, Mao, Bin, Wu
Format: Conference Proceeding
Language:English
Subjects:
fuzzy c-means
Fuzzy entropy clustering
noisy data
possibilistic c-means
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Summary:Fuzzy entropy clustering (FEC) is sensitive to noises the same as fuzzy c-means (FCM) clustering because the probabilistic constraints in their memberships. To solve this noise sensitive problem of FCM, Krishnapuram and Keller have presented the possibilistic c-means (PCM) clustering by abandoning the constraints of FCM. A possibilistic type of fuzzy entropy clustering is proposed based on fuzzy entropy clustering and possibilistic c-means clustering. The proposed algorithm deals with noisy data better than FEC. Furthermore, the parameters of PCM is optimized using possibilistic clustering trick. Our experiments show that FEC is sensitive to noises while our proposed algorithm is insensitive to noises and has better clustering accuracy than FEC.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2011.08.372