Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
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In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, three types of indices (e.g., PC, DI, and DBI) were used. After observing the clustering algorithms, it was evident that each of them has its limitations; however, PCM is more robust to noise than FCM as in case of FCM a noise point has to be considered as a member of any of the cluster.
2006 ◽
Vol 174
(3)
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pp. 1742-1759
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1986 ◽
Vol PAMI-8
(2)
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pp. 248-255
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2011 ◽
Vol 211-212
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pp. 793-797
2013 ◽
Vol 284-287
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pp. 3537-3542
2014 ◽
Vol 57
(11)
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pp. 1-8
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2021 ◽
Vol 14
(1)
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2019 ◽
Vol 9
(2)
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pp. 4778-4784
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1995 ◽
pp. 110-137
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