Overlapping clustering with sparseness constraints

Haibing Lu, Yuan Hong, W. Nick Street, Fei Wang, Hanghang Tong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

Overlapping clustering allows a data point to be a member of multiple clusters, which is more appropriate for modeling many real data semantics. However, much of the existing work on overlapping clustering simply assume that a data point can be assigned to any number of clusters without any constraint. This assumption is not supported by many real contexts. In an attempt to reveal true data cluster structure, we propose sparsity constrained overlapping clustering by incorporating sparseness constraints into an overlapping clustering process. To solve the derived sparsity constrained overlapping clustering problems, efficient and effective algorithms are proposed. Experiments demonstrate the advantages of our overlapping clustering model.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Pages486-494
Number of pages9
DOIs
StatePublished - Dec 1 2012
Event12th IEEE International Conference on Data Mining Workshops, ICDMW 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 10 2012

Publication series

NameProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012

Other

Other12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
CountryBelgium
CityBrussels
Period12/10/1212/10/12

ASJC Scopus subject areas

  • Software

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