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Clustering Validity Function Fusion Method of FCM Clustering Algorithm Based on Dempster–Shafer Evidence Theory

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Abstract

With the deepening of the research on clustering algorithm, clustering validity has become an indispensable part of cluster analysis. However, due to the complexity of data structure and different attributes, any clustering validity function cannot be applied to all datasets, so clustering validity function has been bringing forth new ones. Therefore, this paper proposes a clustering validity function fusion model based on D–S evidence theory (DS-CVFFM), which adopts FCM clustering algorithm as the base algorithm, calculates the values of different validity functions, and then uses the values of different clustering validity functions as the evidence source to construct the basic probability assignment function (BPA). Finally, it integrates with the fusion rules of D–S evidence theory, and outputs the optimal clustering number according to the decision conditions. DS-CVFFM uses the information fusion of multiple clustering validity functions to judge the number of optimal clusters without the need to propose complex validity functions, and avoid the influence of expert factors in the weighted combination clustering validity evaluation method. Finally, 4 sets of artificial datasets and 14 sets of UCI datasets are selected to verify the effectiveness of the proposed model. The experimental results show that compared with the traditional clustering validity evaluation methods, the proposed fusion model has a significant improvement in the accuracy of judging the optimal number of clusters, and the stability is improved under different values of fuzzy exponent, which can overcome the shortcomings of traditional clustering validity evaluation methods.

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Acknowledgements

This work was supported by the Basic Scientific Research Project of Institution of Higher Learning of Liaoning Province (Grant No. 2017FWDF10), and the Project by Liaoning Provincial Natural Science Foundation of China (Grant No. 20180550700).

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Authors and Affiliations

  1. School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan, 114044, China

    Hong-Yu Wang, Jie-Sheng Wang & Guan Wang

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  1. Hong-Yu Wang
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  3. Guan Wang
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Contributions

Hong-yu Wang participated in the data collection, analysis, algorithm simulation, and draft writing. Jie-Sheng Wang participated in the concept, design, interpretation and commented on the manuscript. Guan Wang participated in the critical revision of this paper.

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Correspondence to Jie-Sheng Wang.

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Wang, HY., Wang, JS. & Wang, G. Clustering Validity Function Fusion Method of FCM Clustering Algorithm Based on Dempster–Shafer Evidence Theory. Int. J. Fuzzy Syst. 24, 650–675 (2022). https://doi.org/10.1007/s40815-021-01170-2

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  • DOI: https://doi.org/10.1007/s40815-021-01170-2

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