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Improving fuzzy clustering model for probability density functions using the two-objective genetic algorithm

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Abstract

This paper proposes a fuzzy clustering model for probability density functions (PDFs) using the two-objective genetic algorithm. In this model, the \(L^1\)-distance is used to evaluate the similarity of PDFs, and new two indexes that relate to the similarity of PDFs and clusters are proposed as the objective functions of genetic algorithm. Moreover, the operators for crossover, mutation, and selection are also updated to improve the quality of fuzzy clustering according to the corrected rand, the partition entropy, and the partition coefficients. By combining these improvements, we have an effective automatic fuzzy clustering algorithm for PDFs that can determine the appropriate number of clusters, the elements in each cluster, and the probability belonging to clusters of each element. The proposed model is tested through experiments using the established Matlab procedure, and it is also applied effectively to image data. These experiments demonstrate the superiority of the proposed model compared to other models.

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Data Availability

The authors declare that data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Funding

This research is funded by Ministry of Education and Training in Viet Nam under Grant Number: B2022-TCT-03.

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Authors and Affiliations

  1. Faculty of Mechanical - Electrical and Computer Engineering, School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

    Dinh Phamtoan

  2. College of Natural Science, Can Tho University, Can Tho City, Vietnam

    Tai Vovan

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  1. Dinh Phamtoan
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  2. Tai Vovan
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Correspondence to Tai Vovan.

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Phamtoan, D., Vovan, T. Improving fuzzy clustering model for probability density functions using the two-objective genetic algorithm. Multimed Tools Appl 83, 45291–45314 (2024). https://doi.org/10.1007/s11042-023-17217-5

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  • DOI: https://doi.org/10.1007/s11042-023-17217-5

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