An improved weighted ensemble clustering based on two-tier uncertainty measurement

Gu, Qinghua; Wang, Yan; Wang, Peipei; Li, Xuexian; Chen, Lu; Xiong, Neal N.; Liu, Di

doi:10.1016/j.eswa.2023.121672

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2024

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Cited by 3 publications

References 31 publications

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PCS-granularity weighted ensemble clustering via Co-association matrix

Wu,

Cai,

et al. 2024

Appl Intell

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PCS-granularity weighted ensemble clustering via Co-association matrix

Wu,

Cai,

et al. 2024

Appl Intell

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A feasible solution model for manifold clustering

Wan,

Zhang,

Ran

et al. 2024

Engineering Applications of Artificial Intelligence

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Clustering ensemble based on multiscale cluster reliability

ji,

chen

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Clustering ensemble can effectively improve the accuracy and robustness of clustering. The quality and diversity of base clustering are crucial to the clustering ensemble effect. However, traditional clustering ensemble algorithms usually treat a base clustering result as a whole, ignoring the quality differences within the same base clustering result. Clusters of different quality have completely different contributions to clustering ensemble results, and ignoring the quality differences of clusters can seriously affect the effectiveness of clustering ensemble. To solve this problem, this paper proposes a clustering ensemble method based on multiscale cluster reliability. In this method, multiscale global structural information is first mined by performing random walks on the cluster similarity graph to obtain multiscale correlation between clusters. Then, the multiscale cluster reliability is measured by using the multiscale correlation of clusters, combined with the information entropy, and the clusters are weighted accordingly. Lastly, the clustering results are obtained by graph segmentation. Experimental results on numerous real datasets show that compared with 10 typical and state-of-the-art clustering ensemble methods, the clustering ensemble method based on multiscale cluster reliability not only performs better in clustering effect but also has higher stability.

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An improved weighted ensemble clustering based on two-tier uncertainty measurement

Cited by 3 publications

References 31 publications

PCS-granularity weighted ensemble clustering via Co-association matrix

PCS-granularity weighted ensemble clustering via Co-association matrix

A feasible solution model for manifold clustering

Clustering ensemble based on multiscale cluster reliability

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