DocumentCode :
3661044
Title :
Collaborative clustering with heterogeneous algorithms
Author :
Jérémie Sublime;Nistor Grozavu;Younès Bennani;Antoine Cornuéjols
Author_Institution :
AgroParisTech, UMR INRA MIA 518, 16 rue Claude Bernard, 75231 Paris Cedex 5, France
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1
Lastpage :
8
Abstract :
The aim of collaborative clustering is to reveal the common underlying structures found by different algorithms while analyzing data. The fundamental concept of collaboration is that the clustering algorithms operate locally but collaborate by exchanging information about the local structures found by each algorithm. In this framework, the one purpose of this article is to introduce a new method which allows to reinforce the clustering process by exchanging information between several results acquired by different clustering algorithms. The originality of our proposed approach is that the collaboration step can use clustering results obtained from any type of algorithm during the local phase. This article gives the theoretical foundations of our approach as well as some experimental results. The proposed approach has been validated on several data sets and the results have shown to be very competitive.
Keywords :
"Clustering algorithms","Silicon","Indexes"
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2015 International Joint Conference on
Electronic_ISBN :
2161-4407
Type :
conf
DOI :
10.1109/IJCNN.2015.7280351
Filename :
7280351
Link To Document :
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