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Communication Dans Un Congrès Année : 2012

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

Résumé

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.
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Dates et versions

hal-00750751 , version 1 (12-11-2012)

Identifiants

  • HAL Id : hal-00750751 , version 1

Citer

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. ⟨hal-00750751⟩
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