Parallel multi-view concept clustering in distributed computing

Multi-view clustering (MvC) is an emerging task in data mining. It aims at partitioning the data sampled from multiple views. Although a great deal of research has been done, this task remains to be very challenging. We found an important problem in performing the MvC task. MvC needs large amounts o...

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Veröffentlicht in:Neural computing & applications Jg. 32; H. 10; S. 5621 - 5631
Hauptverfasser: Wang, Hao, Yang, Yan, Zhang, Xiaobo, Peng, Bo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Springer London 01.05.2020
Springer Nature B.V
Schlagworte:
Advances in Parallel and Distributed Computing for Neural Computing
Artificial Intelligence
Clustering
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer networks
Computer Science
Data mining
Data Mining and Knowledge Discovery
Distributed processing
Factorization
Image Processing and Computer Vision
Machine learning
Manifolds (mathematics)
Probability and Statistics in Computer Science
Representations
Multi-view clustering
Manifold learning
Concept factorization
Distributed computing
ISSN:0941-0643, 1433-3058
Online-Zugang:Volltext
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Zusammenfassung:Multi-view clustering (MvC) is an emerging task in data mining. It aims at partitioning the data sampled from multiple views. Although a great deal of research has been done, this task remains to be very challenging. We found an important problem in performing the MvC task. MvC needs large amounts of computation. To address this problem, we propose a parallel MvC method in a distributed computing environment. The proposed method builds upon concept factorization with local manifold learning, denoted by parallel multi-view concept clustering (PMCC). Concept factorization learns a compressed representation for the data. Local manifold learning preserves the locally intrinsic geometrical structure in the data. The weight of each view is learned automatically and a cooperative normalized approach is proposed to better guide the learning of a consensus representation for all views. For the proposed PMCC architecture, the calculation of each part is independent. It is clear that our PMCC can be performed in a distributed computing environment. Experimental results using real-world datasets demonstrate the effectiveness of the proposed method.
Bibliographie:ObjectType-Article-1
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04243-4