Multitask fuzzy Bregman co-clustering approach for clustering data with multisource features

In usual real-world clustering problems, the set of features extracted from the data has two problems which prevent the methods from accurate clustering. First, the features extracted from the samples provide poor information for clustering purpose. Second, the feature vector usually has a high-dimensional multi-source nature, which results in a complex cluster structure in the feature space. In this paper, we propose to use a combination of multi-task clustering and fuzzy co-clustering techniques, to overcome these two problems. In addition, the Bregman divergence is used as the concept of dissimilarity in the proposed algorithm, in order to create a general framework which enables us to use any kind of Bregman distance function, which is consistent with the data distribution and the structure of the clusters. The experimental results indicate that the proposed algorithm can overcome the two mentioned problems, and manages the complexity and weakness of the features, which results in appropriate clustering performances.