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PCS-granularity weighted ensemble clustering via Co-association matrix

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Abstract

Ensemble clustering has attracted much attention for its robustness and effectiveness compared to single clustering. As one of the representative methods, most co-association matrix-based ensemble clustering typically only take into account a single type of information contained in base partitions. This study proposes a new weighted ensemble clustering algorithm of fusing multi-level data information to sufficiently mine the information from the base partition family. Three different levels of data information, including partition granularity level, cluster granularity level and sample granularity level, are concomitantly considered in the co-association matrix. More specifically, we utilize knowledge granularity to measure the quality of base partitions, and rough membership to quantify the credibility of base clusters; Additionally, the relative similarity of a pair of samples is estimated with respect to different base partitions, taking into account the close relationship between samples and the structure of base clusters. Subsequently, the partition-cluster-sample-granularity weighted co-association (PCSCA) matrix is proposed to address the limitations of the co-association matrix, quantifying the quality of information at multiple levels. Finally, this study introduces the partition-cluster-sample-granularity weighted ensemble clustering (PCSEC), which incorporates the PCSCA matrix. The experimental results demonstrate the effectiveness of the proposed method.

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Data Availability

The datasets analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. Synthetic datasets derived from the website: https://github.com/milaan9/Clustering-Datasets/tree/master/02.%20Synthetic.

  2. Real datasets derived from the website: http://archive.ics.uci.edu/ml/datasets.php.

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Funding

The authors would like to thank the editors and anonymous reviewers for their constructive comments. This work is supported by NSFC (No. 12231007), Hunan Provincial Natural Science Foundation of China (No. 2023JJ30113), and Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012342).

Author information

Authors and Affiliations

  1. School of Mathematics, Hunan University, Changsha, 410082, China

    Zhishan Wu, Mingjie Cai, Feng Xu & Qingguo Li

  2. Shenzhen Research Institute, Hunan University, Shenzhen, 518000, China

    Mingjie Cai

Authors
  1. Zhishan Wu
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  2. Mingjie Cai
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  3. Feng Xu
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  4. Qingguo Li
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Mingjie Cai, Feng Xu and Qingguo Li. The first draft of the manuscript was written by Zhishan Wu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Mingjie Cai or Feng Xu.

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The data used in the current study are ethical.

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Cite this article

Wu, Z., Cai, M., Xu, F. et al. PCS-granularity weighted ensemble clustering via Co-association matrix. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05368-3

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  • DOI: https://doi.org/10.1007/s10489-024-05368-3

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