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Feature selection for clustering with constraints using Jensen-Shannon divergence

In semi-supervised clustering, domain knowledge can be converted to constraints and used to guide the clustering. In this paper we propose a feature selection algorithm for semi-supervised clustering. In our method, features are conditionally independent. Feature saliency is first computed in unsupervised clustering using the expectation maximization model. Then, it is refined in the tuning step to minimize the feature-wise constraint violation measure, calculated based on the Jensen-Shannon divergence. Experimental results show that a small amount of supervision can improve the performance of clustering and feature selection.