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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},
        volume={10},
        number={3},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={1},
        keywords={fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, suppression possibilistic fuzzy c-means},
        doi={10.4108/eetsis.v10i3.2057}
    }
    
  • Jyoti Arora
    Meena Tushir
    Shivank Kumar Dadhwal
    Year: 2023
    A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm
    SIS
    EAI
    DOI: 10.4108/eetsis.v10i3.2057
Jyoti Arora1,*, Meena Tushir1, Shivank Kumar Dadhwal1
  • 1: Maharaja Surajmal Institute of Technology, New Delhi, India
*Contact email: joy.arora@gmail.com

Abstract

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.

Keywords
fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, suppression possibilistic fuzzy c-means
Received
2022-07-19
Accepted
2022-12-22
Published
2023-01-03
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.v10i3.2057

Copyright © 2023 J. Arora et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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