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A fuzzy clustering ensemble selection based on active full-link similarity

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Abstract

In fuzzy clustering ensemble, the quality of fuzzy base clustering has an important influence on the performance of the final clustering result. Due to the performance of fuzzy clustering is affected by the initial parameters and fuzzy factors, it may cause unstable clustering results such as unsatisfactory data affiliation, and large differences with the distribution of the real data set. In addition, how to use fuzzy ensemble information to determine the similarity among samples effectively plays a crucial role in the generation of co-association matrix elements. In view of the above problems, combined with the compactness, separation and overlap in the evaluation index of fuzzy clustering, an optimized fuzzy clustering evaluation index is designed to select high quality fuzzy base clustering members to participate the final fusion. Then, the concept of sample attribution clarity is proposed, and the attribution clarity of each sample in the fuzzy base clustering set is learned actively. For samples with different attribution clarity, different full-link similarity measurement methods between samples are designed to further reduce the uncertainty of samples. Finally, the clustering results are obtained by the agglomerative hierarchical clustering. In order to verify the effectiveness of the proposed method, ten data sets are used to conduct experiments. Experiments show that the results obtained by the proposed method are closer to the real distribution structure of the data set in most experimental dataset, and are not sensitive to the diversity of base clustering members, and have good robustness.

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Data availability

The experimental data sets in this paper are all from the UCI Machine Learning repository.

References

  1. Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Net 16(3):645–678

    Article  Google Scholar 

  2. Saxena A, Prasad M, Gupta A et al (2017) A review of clustering techniques and developments. Neurocomputing 267:664–681

    Article  Google Scholar 

  3. Yuan KH, Xu WH, Li WT et al (2022) An incremental learning mechanism for object classificationbased on progressive fuzzy three-way concept. Inf Sci 584(1):127–147

    Article  Google Scholar 

  4. Zhou P, Wang X, Du L et al (2022) Clustering ensemble via structured hypergraph learning. Inform Fusion 78:171–179

    Article  Google Scholar 

  5. Chen Z, Bagherinia A, Minaei-Bidgoli B et al (2021) Fuzzy clustering ensemble considering cluster dependability. Int J Artif Intell Tools 30(2):2150007

    Article  Google Scholar 

  6. Xu WH, Guo DD, Qian YH et al (2022) Two-way concept-cognitive learning method: a fuzzy-based progressive learning. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2022.3216110

    Article  Google Scholar 

  7. Xu WH, Yuan KH, Li WT et al (2023) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Trans Emerg Top comp Intel 7(1):76–88

    Article  Google Scholar 

  8. Bagherinia A, Minaei-Bidgoli B, Hossinzadeh M et al (2019) Elite fuzzy clustering ensemble based on clustering diversity and quality measures. Appl Intell 49:1724–1747

    Article  Google Scholar 

  9. Banerjee A, Pujari A, Rani Panigrahi C et al (2021) A new method for weighted ensemble clustering and coupled ensemble selection. Connect Sci 33(3):623–644

    Article  Google Scholar 

  10. Mojarad M, Nejatian S, Parvin H et al (2019) A fuzzy clustering ensemble based on cluster clustering and iterative Fusion of base clusters. Appl Intell 49:2567–2581

    Article  Google Scholar 

  11. Bagherinia A, Minaei-Bidgoli B, Hossinzadeh M et al (2021) Reliability-Based Fuzzy Clustering Ensemble. Fuzzy Sets Syst 413:1–28

    Article  MathSciNet  MATH  Google Scholar 

  12. Li WJ, Wang ZK, Sun W et al (2022) An ensemble clustering framework based on hierarchical clustering ensemble selection and clusters clustering. Cybern Syst. https://doi.org/10.1080/01969722.2022.2073704

    Article  Google Scholar 

  13. Wang YX, Yuan LP, Garg H et al (2021) Information theoretic weighted fuzzy clustering ensemble. Cmc-Comp Mater Cont 67(1):369–392

    Google Scholar 

  14. Bai L, Liang JY, Guo YK (2018) An ensemble clusterer of multiple fuzzy k-means clusterings to recognize arbitrarily shaped clusters. IEEE Trans Fuzzy Syst 26(6):3524–3533

    Google Scholar 

  15. Rathore P, Bezdek JC, Erfani SM et al (2018) Ensemble fuzzy clustering using cumulative aggregation on random projections. IEEE Trans Fuzzy Syst 26(3):1510–1524

    Article  Google Scholar 

  16. Liu HQ, Zhang Q, Zhao F (2018) Interval fuzzy spectral clustering ensemble algorithm for color image segmentation. J Intel Fuzzy Syst 35(5):5467–5476

    Article  Google Scholar 

  17. Iam-On N, Boongoen T, Garrett S (2010) LCE: a link-based cluster ensemble method for improved gene expression data analysis. Bioinformatics 26(12):1513–1519

    Article  Google Scholar 

  18. Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10(2):191–203

    Article  Google Scholar 

  19. Jiang CM, Li ZC, Yao JT (2022) A shadowed set-based three-way clustering ensemble approach. Int J Mach Learn Cyber 13(9):2545–2558

    Article  Google Scholar 

  20. Zhang MM (2022) Weighted clustering ensemble: a review. Pattern Recogn 124:108428

    Article  Google Scholar 

  21. Hu J, Li TR, Luo C et al (2017) Incremental fuzzy cluster ensemble learning based on rough set theory. Knowl-Based Syst 132:144–155

    Article  Google Scholar 

  22. Su P, Shang C, Shen Q. 2014 Link-based pairwise similarity matrix approach for fuzzy c-means clustering ensemble. IEEE International Conference on Fuzzy Systems. IEEE 1538–1544

  23. Wu S, Jiang QS, Hong ZL, et al. 2006 A Novel Fuzzy Cluster Validity Index with New Compositions. Proc. of the 6th World Congress on Intelligent Control and Automation, 5967 -5971.

  24. Tang MH, Yang Y, Zhang WB. 2009 An improved clustering validity function for the fuzzy cmeans algorithm. Proc. of the 4th International Conference on Intelligent Systems and Knowledge Engineering, 209–214.

  25. Chen J M. 2012 The improved partition entropy coefficient. Multimedia and Signal Processing: Second International Conference, CMSP 2012, Shanghai, China. Springer Berlin Heidelberg, 1-7

  26. Rashidi F, Nejatian S, Parvin H et al (2019) Diversity based cluster weighting in cluster ensemble: an information theory approach. Artif Intell Rev 52(2):1341–1368

    Article  Google Scholar 

  27. Xu WH, Guo DD, Mi JS et al (2023) Two-way concept-cognitive learning via concept movement viewpoint. IEEE Trans Neural Net Lear Syst. https://doi.org/10.1109/TNNLS.2023.3235800

    Article  Google Scholar 

  28. Xu WH, Pan YZ, Chen XW et al (2022) a novel dynamic fusion approach using information entropy for interval-valued ordered datasets. IEEE Trans Big Data. https://doi.org/10.1109/TBDATA.2022.3215494

    Article  Google Scholar 

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Acknowledgements

This work is supported by National Natural Science Foundation of China (No.62206240), the Shandong Provincial Natural Science Foundation of China (No. ZR2020QF110), and Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (GUANGXI MINZU UNIVERSITY)(No. GXIC20-04).

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Authors and Affiliations

  1. College of Information Science and Engineering, ZaoZhuang University, Zaozhuang, 277160, China

    Li Xu, XiaoFei Yan, Jie Huang, Yanqiu Wang & Zeng Li

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  1. Li Xu
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  2. XiaoFei Yan
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  3. Jie Huang
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  4. Yanqiu Wang
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  5. Zeng Li
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Correspondence to Li Xu or XiaoFei Yan.

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Xu, L., Yan, X., Huang, J. et al. A fuzzy clustering ensemble selection based on active full-link similarity. Int. J. Mach. Learn. & Cyber. 14, 4325–4337 (2023). https://doi.org/10.1007/s13042-023-01896-5

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