Clustering Ensemble Based on Hybrid Multiview Clustering
| Autor: | Xian-Bing Meng, Daxing Wang, Zhiwen Yu, C. L. Philip Chen |
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| Rok vydání: | 2022 |
| Předmět: |
Computer science
computer.software_genre Base (topology) Fuzzy logic Computer Science Applications k-nearest neighbors algorithm Human-Computer Interaction ComputingMethodologies_PATTERNRECOGNITION Transformation (function) Control and Systems Engineering Principal component analysis Key (cryptography) Cluster Analysis Learning Data mining Electrical and Electronic Engineering Cluster analysis computer Algorithms Software Subspace topology Information Systems |
| Zdroj: | IEEE Transactions on Cybernetics. 52:6518-6530 |
| ISSN: | 2168-2275 2168-2267 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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