Towards conflict resolution in collaborative clustering

In recent years, a lot of work has focused on the use of multiple clusterings for partitioning data. These approaches are supported by the existence of a huge number of clustering algorithms. Thus, different methods have been proposed to create alternative clustering results from the same data. However, the different clustering results are usually generated without sharing information and the user is often asked to select the final result. To cope with these issues, a new paradigm named collaborative clustering has been proposed recently. In collaborative clustering, different clustering methods work together (i.e. collaborate) to reach an agreement on the clustering of a common dataset. At the end of the collaboration, the results are expected to be strongly similar. In this paper, we address the problem of the collaboration of different clustering methods and we compare four collaboration strategies. Our experiments compare the different strategies on synthetic and real-life datasets and provide insight into the advantages and the drawbacks of each strategy.