Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering

Authors

  • Wang Hengda School of Computing, Universiti Utara Malaysia, Malaysia and Artificial Intelligence Department, Chongqing Institute of Engineering, China
  • Mohamad Farhan Mohamad Mohsin School of Computing, Universiti Utara Malaysia, Malaysia
  • Muhammad Syafiq Mohd Pozi School of Computing, Universiti Utara Malaysia, Malaysia

DOI:

https://doi.org/10.32890/jict2024.23.3.6

Keywords:

Fuzzy clustering, beta distribution, feature weighted, ordered mechanism

Abstract

The fuzzy C-ordered-means clustering (FCOM) is a fuzzy clustering algorithm that enhances robustness and clustering accuracy through the ordered mechanism based on fuzzy C-means (FCM). However, despite these improvements, the FCOM algorithm’s effectiveness remains unsatisfactory due to the significant time cost incurred by its ordered operation. To address this problem, an investigation was conducted on the ordered weighted model of the FCOM algorithm leading to proposed enhancements by introducing the beta distribution weighted fuzzy C-ordered-means clustering (BDFCOM). The BDFCOM algorithm utilises the properties of the Beta distribution to weight sample features, thus not only circumventing the time cost problem of the traditional ordered mechanism but also reducing the influence of noise. Experiments were conducted on six UCI datasets to validate the effectiveness of the BDFCOM, comparing its performance against seven other clustering algorithms using six evaluation indices. The results show that compared to the average of the other seven algorithms, BDFCOM improves about 15 percent on F1-score, 11 percent on Rand Index, 13 percent on Adjusted Rand Index, 3 percent on Fowlkes-Mallows Index and 16 percent on Jaccard Index. For the other two ordered mechanism FCM algorithms, the time consumption was also reduced by 90.15 percent on average. The proposed algorithm, which designs a new way of feature weighting for ordered mechanisms, advances the field of ordered mechanisms.
And, this paper provides a new method in the application field where there is a lot of noise in the dataset. 

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Published

28-07-2024

Issue

Vol. 23 No. 3 (2024): Journal of Information and Communication Technology (JICT) Vol.23, No.3, July 2024

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How to Cite

Wang, H., Mohamad Mohsin, M. F., & Mohd Pozi, M. S. (2024). Beta Distribution Weighted Fuzzy C-Ordered-Means Clustering. Journal of Information and Communication Technology, 23(3), 523-559. https://doi.org/10.32890/jict2024.23.3.6

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Identifiers DOI 10.32890/jict2024.23.3.6 OpenAlex W4401102313 Scopus 85200132203

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