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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.
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https://hal.archives-ouvertes.fr/hal-00750751
Contributor : Gilles Bisson <>
Submitted on : Monday, November 12, 2012 - 3:06:19 PM
Last modification on : Monday, April 20, 2020 - 11:24:01 AM

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  • HAL Id : hal-00750751, version 1

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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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