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A New Approach for Semi-supervised Fuzzy Clustering with Multiple Fuzzifiers

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Abstract

Data clustering is the process of dividing data elements into different clusters in which elements in one cluster have more similarity than those in other clusters. Semi-supervised fuzzy clustering methods are used in various applications. The available methods are based on fuzzy C-Mean, kernel function, weight function and adaptive function. The fuzzification coefficient is an important factor that affects to the performance in these methods. In this paper, we propose the improvements of semi-supervised standard fuzzy C-Mean clustering (SSFCM) by using multiple fuzzifiers to increase clusters quality. Two proposed models, named as MCSSFC-P and MCSSFC-C, use different fuzzifiers for each data point and for each cluster, respectively, which are established in a form of optimal problems. The values of fuzzifiers are updated to get the best values of objective functions. Evaluations on different datasets are performed. The numerical results show the higher performance of our model than some related models.

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Acknowledgements

This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant number. 102.05-2020.11.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

    Tran Manh Tuan & Tran Thi Ngan

  2. Institute of Techniques for Special Engineering, Le Quy Don Technical University, Hanoi, Vietnam

    Mai Dinh Sinh

  3. School of Information and Communication Technology, Hanoi University of Science and Technology, Hanoi, Vietnam

    Tran Đinh Khang

  4. University of Information and Communication Technology, Thai Nguyen University, Thainguyen, Vietnam

    Phung The Huan & Vu Duc Thai

  5. Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Nguyen Long Giang

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  1. Tran Manh Tuan
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  2. Mai Dinh Sinh
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Correspondence to Tran Manh Tuan.

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Tuan, T.M., Sinh, M.D., Khang, T.Đ. et al. A New Approach for Semi-supervised Fuzzy Clustering with Multiple Fuzzifiers. Int. J. Fuzzy Syst. 24, 3688–3701 (2022). https://doi.org/10.1007/s40815-022-01363-3

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  • DOI: https://doi.org/10.1007/s40815-022-01363-3

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