Co-clustering of multi-view datasets

In many clustering problems, we have access to multiple sources of data representing different aspects of the problem. Each of these data separately represents an association between entities. Multi-view clustering involves integrating clustering information from these heterogeneous sources of data and has been shown to improve results over a single-view clustering. On the other hand, co-clustering has been widely used as a technique to improve clustering results on a single view by exploiting the duality between objects and their attributes. In this paper, we propose a multi-view clustering setting in the context of a co-clustering framework. Our underlying assumption is that similarity values generated from the individual data can be transferred from one view to the other(s) resulting in a better clustering of the data. We provide empirical evidence to show that this framework results in a better clustering accuracy than those obtained from any of the single views, tested on different datasets.