Analysis of the influence of diversity in collaborative and multi-view clustering

Publikationstyp
Conference Paper
Date Issued
2017-05
Sprache
English
Author(s)
Sublime, Jérémie  
Matei, Basarab  
Murena, Pierre Alexandre  
TORE-URI
http://hdl.handle.net/11420/15259
Start Page
4126
End Page
4133
Article Number
7966377
Citation
International Joint Conference on Neural Networks (IJCNN 2017)
Contribution to Conference
2017 International Joint Conference on Neural Networks, IJCNN 2017  
Publisher DOI
10.1109/IJCNN.2017.7966377
Scopus ID
2-s2.0-85030994162
Multi-source clustering is common data mining task the aim of which is to use several clustering algorithms to analyze different aspects of the same data. Well known applications of multi-source clustering include horizontal collaborative clustering and multi-view clustering, where several algorithms combine their strengths by exchanging information about their finding on local structures with a goal of mutual improvement. However, many of these proposed algorithms and statistical models lack the capability to detect weak collaborations that may prove detrimental to the global clustering process. In this article, we propose a weighing optimization method that will help detecting which algorithms should exchange their information based on the diversity between the different algorithms' solutions.