Improved Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: Improved PIFCM
Abstract: Recently proposed Probabilistic Intuitionistic Fuzzy c-Means Algorithm (PIFCM) is a Probabilistic Euclidian distance measure (PEDM) based clustering technique, which incorporate computation of probabilistic intervals (Pij, Qij) for each of the data point. PIFCM algorithm employs a random membership function $\frac{1}{{\left| x \right|}}$ and discards a data point if its membership value is uniformly distributed in the clusters. Fuzzy clustering always gets affected by the choice of the membership function. Accordingly, in PIFCM algorithm, membership function changes the properties of the data limiting its capabilities in giving consistent clustering results. Moreover, PIFCM algorithm incorporates computation of redundant matrices while finding Pij and Qij. In this paper, we propose some novel changes in the existing PIFCM algorithm, and hence introduce our Improved PIFCM algorithm. The improved PIFCM algorithm considers the min-max normalization as membership function, and also removes the redundant matrix computation that was used to find the Pij and Qij in the original PIFCM. Results over various UCI datasets validates the superiority of our improved PIFCM algorithm over FCM algorithm, IFCM algorithm and PIFCM algorithm.
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