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<p>Clustering ensemble is a powerful technique for aggregating multiple clustering results. In order to address the challenge in clustering analysis which was brought by the uncertainty information in the datasets, this work presents a novel three-way clustering ensemble method based on shadowed sets with five approximation regions (3WCE-S5). Firstly, a set of clustering members are generated by fuzzy c-means clustering (FCM). A new shadowed sets is approximated by five regions, named as shadowed sets with five approximation regions (S5). Then, all objects are initially partitioned into five regions according to their membership degrees, which are provided by FCM. Secondly, according to multi-granularity rough sets, objects are further assigned into six approximated regions, namely a core region and five fringe regions. There is a partial order relationship between these six different approximate regions. Finally, the above six regions are processed by the new shadowed sets again to generate the output of three-way clustering. Ten University of California Irvine (UCI) data sets are employed to test the performance of this approach and five comparative methods. Accuracy (ACC), adjusted rand index (ARI), normalized mutual information (NMI) and time cost are utilized to quantify the clustering results.</p>

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Three-way clustering ensemble based on shadowed sets with five approximation regions

  • 期刊论文

  • 发表日期：

    2025年9月25日

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  • Huangjian Yi,
  • Dongkai Guo,
  • Qinran Zhang,
  • Xiaowei He,
  • Ruisi Ren

摘要

Clustering ensemble is a powerful technique for aggregating multiple clustering results. In order to address the challenge in clustering analysis which was brought by the uncertainty information in the datasets, this work presents a novel three-way clustering ensemble method based on shadowed sets with five approximation regions (3WCE-S5). Firstly, a set of clustering members are generated by fuzzy c-means clustering (FCM). A new shadowed sets is approximated by five regions, named as shadowed sets with five approximation regions (S5). Then, all objects are initially partitioned into five regions according to their membership degrees, which are provided by FCM. Secondly, according to multi-granularity rough sets, objects are further assigned into six approximated regions, namely a core region and five fringe regions. There is a partial order relationship between these six different approximate regions. Finally, the above six regions are processed by the new shadowed sets again to generate the output of three-way clustering. Ten University of California Irvine (UCI) data sets are employed to test the performance of this approach and five comparative methods. Accuracy (ACC), adjusted rand index (ARI), normalized mutual information (NMI) and time cost are utilized to quantify the clustering results.