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3WC-D: A feature distribution-based adaptive three-way clustering method

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Abstract

Clustering is a significant unsupervised learning method in the machine learning field, which can mine the distribution pattern and attribute of data. However, traditional clustering methods can not fully represent the attribution relationship between objects and classes. Therefore, a three-way clustering (3WC), which combines three-way decision (3WD) with clustering, has gradually received widespread attention from researchers in recent years. However, existing 3WC methods mostly use traditional clustering results or randomly assigned results as initial division results, which largely ignore the distribution relation of each object. Moreover, most of 3WC methods are soft clustering, i.e., there are some objects that will belong to more than one class, which makes clustering results more ambiguous. In light of this situation, we establish a feature distribution-based adaptive three-way clustering (3WC-D) method to address the above challenge. First, 3WC-D utilizes 3WD to characterize the distribution relation of objects for obtaining initial clustering results. Then, several representative classes are selected for further processing based on the interrelationship among classes in initial clustering results. Finally, the remaining objects are divided according to the relative relation between objects and classes, so as final clustering results can be obtained, and the effectiveness of the method is illustrated by comparing with several clustering methods on diverse datasets.

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Acknowledgements

The authors are extremely grateful to the editors and five anonymous referees for their valuable comments which helped us improve the presentation of this article.

The research was partially supported by grants from NNSFC (12161036; 61866011; 12271146) and a Discovery Grant from NSERC, Canada.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Hubei Minzu University, Enshi, 445000, China

    Rongtao Zhang, Xueling Ma & Jianming Zhan

  2. Department of Computer Science, University of Regina, Regina, S4S 0A2, Saskatchewan, Canada

    Yiyu Yao

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  1. Rongtao Zhang
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  2. Xueling Ma
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  3. Jianming Zhan
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  4. Yiyu Yao
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Contributions

Rongtao Zhang: Conceptualization, Methodology, Investigation, Writing-original draft. Xueling Ma: Methodology, Investigation, Writing-original draft. Jianming Zhan: Methodology, Writing-Reviewing and Editing. Yiyu Yao: Methodology, Writing-Reviewing and Editing.

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Correspondence to Xueling Ma or Jianming Zhan.

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Zhang, R., Ma, X., Zhan, J. et al. 3WC-D: A feature distribution-based adaptive three-way clustering method. Appl Intell 53, 15561–15579 (2023). https://doi.org/10.1007/s10489-022-04332-3

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