Active constrained fuzzy clustering: A multiple kernels learning approach

In this paper, we address the problem of constrained clustering along with active selection of clustering constraints in a unified framework. To this aim, we extend the improved possibilistic c-Means algorithm (IPCM) with a multiple kernels learning setting under supervision of side information. By incorporating multiple kernels, the limitation of improved possibilistic c-means to spherical clusters is addressed by mapping non-linear separable data to appropriate feature space. The proposed method is immune to inefficient kernels or irrelevant features by automatically adjusting the weight of kernels. Moreover, extending IPCM to incorporate constraints, its strong robustness and fast convergence properties are inherited by the proposed method. In order to avoid querying inefficient or redundant clustering constraints, an active query selection heuristic is embedded into the proposed method to query the most informative constraints. Experiments conducted on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.