SMVC: Semi-supervised multi-view clustering in subspace projections

Since data is often multi-faceted in its very nature, it might not adequately be summarized by just a single clustering. To better capture the data's complexity, methods aiming at the detection of multiple, alternative clusterings have been proposed. Independent of this research area, semi-supervised clustering techniques have shown to substantially improve clustering results for single-view clustering by integrating prior knowledge. In this paper, we join both research areas and present a solution for integrating prior knowledge in the process of detecting multiple clusterings. We propose a Bayesian framework modeling multiple clusterings of the data by multiple mixture distributions, each responsible for an individual set of relevant dimensions. In addition, our model is able to handle prior knowledge in the form of instance-level constraints indicating which objects should or should not be grouped together. Since a priori the assignment of constraints to specific views is not necessarily known, our technique automatically determines their membership. For efficient learning, we propose the algorithm SMVC using variational Bayesian methods. With experiments on various real-world data, we demonstrate SMVC's potential to detect multiple clustering views and its capability to improve the result by exploiting prior knowledge.