2010 UnifyingDependentClusteringandD

From GM-RKB
Jump to navigation Jump to search

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Clustering, relational clustering, contingency tables, multi-criteria optimization.

Abstract

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.

References

,

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2010 UnifyingDependentClusteringandDNaren Ramakrishnan
Ian Davidson
M. Shahriar Hossain
Satish Tadepalli
Layne T. Watson
Richard F. Helm
Unifying Dependent Clustering and Disparate Clustering for Non-homogeneous DataKDD-2010 Proceedings10.1145/1835804.18358802010