Multiview Clustering of Multilingual Documents

YoungMin Kim (2), Massih-Reza Amini(1), Cyril Goutte (1), Patrick Gallinari (2)
(1) National Research Council Canada             (2) Laboratoire d'Informatique Paris 6
                     123, boulevard Alexandre Taché                   104, avenue du président Kennedy              
    Gatineau, Canada                                               75016 Paris         

We propose a new multi-view clustering method which uses clustering results obtained on each view as a voting pattern in order to construct a new set of multi-view clusters. Our experiments on a multilingual corpus of documents show that performance increases significantly over simple concatenation and another multi-view clustering technique.