Co-clustering of Multi-view Datasets: A Parallelizable Approach

In many applications, entities of the domain are described through different views that clustering methods often process one by one. We introduce here the architecture MVSim, that is able to deal simultaneously with all the information contained in such multi-view datasets by using several instances of a co-similarity algorithm. We show that this architecture provides better results than both single-view and multi-view approaches and that it can be easily parallelized thus reducing both time and space complexities of the computations.