Preprint Article Version 1 This version is not peer-reviewed

Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation

Version 1 : Received: 23 November 2018 / Approved: 26 November 2018 / Online: 26 November 2018 (10:19:05 CET)

How to cite: Siddique, M.A.B.; Arif, R.B.; Khan, M.M.R.; Ashrafi, Z. Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation. Preprints 2018, 2018110581 (doi: 10.20944/preprints201811.0581.v1). Siddique, M.A.B.; Arif, R.B.; Khan, M.M.R.; Ashrafi, Z. Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation. Preprints 2018, 2018110581 (doi: 10.20944/preprints201811.0581.v1).

Abstract

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.

Subject Areas

Two-dimensional clustering, Soft clustering, Fuzzy c-means(FCM), Possibilistic c-means (PCM), cluster tendency, VAT algorithm, cluster validation, PC, DI, DBI, noise point

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