Modified Probabilistic Intuitionistic Fuzzy c-Means Clustering Algorithm: MPIFCM
Debanjan Chakraborty, Ayush K. Varshney, Pranab Kumar Muhuri, Q. M. Danish Lohani · 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · 2022
The recently reported ‘Improved Probabilistic Intuitionistic Fuzzy c-Means (IPIFCM) algorithm’ is a computationally efficient algorithm that does fuzzy clustering based on Probabilistic Euclidean Distance measure (PEDM). A significant issue with the IPIFCM algorithm is that it does not consider the hesitation factor's effect while updating the membership of a datapoint for a given cluster. Therefore, the convergence of the algorithm is not optimal. In this paper, we modify the membership function by adding the hesitation component to the IPIFCM clustering algorithm's objective function to propose 'Modified Improved Probabilistic Intuitionistic Fuzzy c-Means' (MPIFCM) clustering algorithm. The proposed MPIFCM algorithm helps in achieving a realistic clustering of the datapoints. This modification leads to the improvement in the accuracy as well as the convergence rate of the algorithm. Experiments over various benchmark UCI datasets confirm that our proposed algorithm provides better performance over its existing counterparts. Popular performance metrices such as accuracy, convergence rate, partition coefficients and cluster entropy are considered for comparative analysis of the performances of the studied algorithms.