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2010
ACM

Unifying dependent clustering and disparate clustering for non-homogeneous data

9 years 9 months ago
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.
M. Shahriar Hossain, Satish Tadepalli, Layne T. Wa
Added 15 Aug 2010
Updated 15 Aug 2010
Type Conference
Year 2010
Where KDD
Authors M. Shahriar Hossain, Satish Tadepalli, Layne T. Watson, Ian Davidson, Richard F. Helm, Naren Ramakrishnan
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