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• W3111181422 abstract "As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data." @default.
• W3111181422 created "2020-12-21" @default.
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• W3111181422 creator A5046710292 @default.
• W3111181422 creator A5049531727 @default.
• W3111181422 date "2022-07-01" @default.
• W3111181422 modified "2023-10-16" @default.
• W3111181422 title "Clustering Ensemble Based on Hybrid Multiview Clustering" @default.
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• W3111181422 doi "https://doi.org/10.1109/tcyb.2020.3034157" @default.
• W3111181422 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33284761" @default.
• W3111181422 hasPublicationYear "2022" @default.
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