An information theory based approach to multisource clustering

Publikationstyp
Conference Paper
Date Issued
2018-07
Sprache
English
Author(s)
Murena, Pierre Alexandre  
Sublime, Jérémie  
Matei, Basarab  
Cornuéjols, Antoine  
TORE-URI
http://hdl.handle.net/11420/15255
Start Page
2581
End Page
2587
Citation
27th International Joint Conference on Artificial Intelligence (IJCAI 2018)
Contribution to Conference
27th International Joint Conference on Artificial Intelligence, IJCAI 2018  
Publisher DOI
10.24963/ijcai.2018/358
Scopus ID
2-s2.0-85055724561
ISBN of container
9780999241127
Clustering is a compression task which consists in grouping similar objects into clusters. In real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. In this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. Using this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.
DDC Class
620: Ingenieurwissenschaften