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.