document: Unifying dependent clustering and disparate clustering for non-homogeneous data Modern data mining settings involve a combination of attribute-valued descriptors over entities as well as specified relationships between these entities . We present an approach to cluster such non-homogeneous datasets by using the relationships to impose either dependent clustering or disparate clustering constraints . Unlike prior work that views constraints as boolean criteria , we present a formulation that allows constraints to be satisfied or violated in a smooth manner . This enables us to achieve dependent clustering and disparate clustering using the same optimization framework by merely maximizing versus minimizing the objective function . We present results on both synthetic data as well as several real-world datasets .
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