Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadata
Contributor : Gilles Bisson <>
Submitted on : Monday, November 12, 2012 - 3:06:19 PM
Last modification on : Friday, November 20, 2020 - 2:54:16 PM


  • HAL Id : hal-00750751, version 1



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⟩



Record views