Co-clustering of Multi-View Datasets: a Parallelizable Approach

Abstract : In many applications, entities of the domain are described through different aspects, or views, that classical clustering methods often process one by one. We introduce here a general architecture, named MVSim, that is able to deal simultaneously with all the information contained in such multi-view datasets by using several instances of an existing co-similarity algorithm. We show that this architecture offers an interesting formal framework to work with multi- view data, and experimentally provides better results than both single-view and multi-view approaches. Furthermore, this architecture can be easily parallelize thus reducing both time and space complexities of the computations.
Type de document :
Communication dans un congrès
ICDM 2012 - International Conference on Data Mining, Dec 2012, Bruxelles, Belgium. 9p., 2012
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00750751
Contributeur : Gilles Bisson <>
Soumis le : lundi 12 novembre 2012 - 15:06:19
Dernière modification le : mardi 28 octobre 2014 - 18:34:42

Identifiants

  • HAL Id : hal-00750751, version 1

Collections

Citation

Gilles Bisson, Clément Grimal. Co-clustering of Multi-View Datasets: a Parallelizable Approach. ICDM 2012 - International Conference on Data Mining, Dec 2012, Bruxelles, Belgium. 9p., 2012. <hal-00750751>

Partager

Métriques

Consultations de la notice

183