Clustering Ensemble Based on Hybrid Multiview Clustering

• Published in: IEEE transactions on cybernetics Vol. PP; no. 7; pp. 1 - 13
• Main Authors: Yu, Zhiwen, Wang, Daxing, Meng, Xian-Bing, Chen, C. L. Philip
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cluster ensemble; Clustering; Clustering algorithms; ensemble learning; Evolutionary computation; Fuses; Kernel; Machine learning; Machine learning algorithms; multiview clustering; Optimization; Principal components analysis; random subspace transformation; Research and development; Transformations
• ISSN: 2168-2267; 2168-2275
• DOI: 10.1109/TCYB.2020.3034157

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.