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

Authors

  • Jyoti Arora Maharaja Surajmal Institute of Technology, New Delhi, India
  • Meena Tushir Maharaja Surajmal Institute of Technology, New Delhi, India
  • Shivank Kumar Dadhwal Maharaja Surajmal Institute of Technology, New Delhi, India

DOI:

https://doi.org/10.4108/eetsis.v10i3.2057

Keywords:

fuzzy c-means, possibilistic c-means, possibilistic fuzzy c-means, suppression possibilistic fuzzy c-means

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.

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Published

03-01-2023

How to Cite

1.
Arora J, Tushir M, Dadhwal SK. A New Suppression-based Possibilistic Fuzzy c-means Clustering Algorithm. EAI Endorsed Scal Inf Syst [Internet]. 2023 Jan. 3 [cited 2024 May 25];10(3):e3. Available from: https://publications.eai.eu/index.php/sis/article/view/2057