Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation
This is an incremental study comparing existing clustering methods for data analysis, with limited practical impact.
The paper applied Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM) clustering algorithms to two-dimensional scenarios, finding that PCM is more robust to noise than FCM because FCM forces noise points into clusters.
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.