Unifying dependent clustering and disparate clustering for non-homogeneous data

Modern data mining settings involve a combination of attributevalued 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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications - Data Mining; I.2.6 [Artificial Intelligence]: Learning General Terms: Algorithms, Measurement, Experimentation.

• Type: Conference
• Year: 2010
• Where: KDD
• Authors: M. Shahriar Hossain, Satish Tadepalli, Layne T. Watson, Ian Davidson, Richard F. Helm, Naren Ramakrishnan
• Keywords: Data Mining, Dependent Clustering, Disparate Clustering, Disparate Clustering Constraints, KDD 2010