sis 23(3): e3

Research Article

A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm

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  • @ARTICLE{10.4108/eetsis.v10i3.2057,
        author={Jyoti Arora and Meena Tushir and Shivank Kumar Dadhwal},
        title={A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        keywords={fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, suppression possibilistic fuzzy c-means},
  • Jyoti Arora
    Meena Tushir
    Shivank Kumar Dadhwal
    Year: 2023
    A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm
    DOI: 10.4108/eetsis.v10i3.2057
Jyoti Arora1,*, Meena Tushir1, Shivank Kumar Dadhwal1
  • 1: Maharaja Surajmal Institute of Technology, New Delhi, India
*Contact email:


Possibilistic fuzzy c-means (PFCM) is one of the most widely used clustering algorithm that solves the noise sensitivity problem of Fuzzy c-means (FCM) and coincident clusters problem of possibilistic c-means (PCM). Though PFCM is a highly reliable clustering algorithm but  the efficiency of the algorithm can be further improved by introducing the concept of suppression. Suppression-based algorithms employ the winner and non-winner based suppression technique on the datasets, helping in performing better classification of real-world datasets into clusters. In this paper, we propose a suppression-based possibilistic fuzzy c-means clustering algorithm (SPFCM) for the process of clustering. The paper explores the performance of the proposed methodology based on number of misclassifications for various real datasets and synthetic datasets and it is found to perform better than other clustering techniques in the sequel, i.e., normal as well as suppression-based algorithms. The SPFCM is found to perform more efficiently and converges faster as compared to other clustering techniques.