Active constrained fuzzy clustering: A multiple kernels learning approach

• Active constrained clustering is examined in this paper.
• The proposed method relies on a multiple kernels learning setting.
• The method deals with linear inseparable, partially overlapping and noisy datasets.
• An active query selection heuristic was embedded into the clustering algorithm.
• The query selection heuristic is based on the measurement of mistake in clustering.

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 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.