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Suppressed possibilistic c-means clustering algorithm
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DOI：10.1016/j.asoc.2019.02.027.png)
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摘要
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The possibilistic c-means (PCM) clustering algorithm always suffers from a coincident clustering problem since it relaxes the probabilistic constraint in the fuzzy c-means (FCM) clustering algorithm. In this paper, to overcome the shortcoming of the PCM, a novel suppressed possibilistic c-means (S-PCM) clustering algorithm by introducing a suppressed competitive learning strategy into the PCM so as to improve the between-cluster relationships is proposed. Specifically, in the updating process the new algorithm searches for the biggest typicality which is regarded as winner by a competitive mechanism. Then it suppresses the non-winner typicalities with a suppressed rate which is used to control the learning strength. Moreover, the parameter setting problems of the suppressed rate and the penalty parameter in the S-PCM are also discussed in detail. In addition, the suppressed competitive learning strategy is still introduced into the possibilistic Gustafson-Kessel (PGK) clustering algorithm and a novel suppressed possibilistic Gustafson-Kessel (S-PGK) clustering model is proposed, which is more applicable to the ellipsoidal data clustering. Finally, experiments on several synthetic and real datasets with noise injection demonstrate the effectiveness of the proposed algorithms. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Suppressed fuzzy c-means clustering
Possibilistic c-means clustering
Possibilistic Gustafson-Kessel clustering
Suppressed rate
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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OAAI分享收藏Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm: a study on the competition in c-means clustering models.png)
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