Conference proceeding
Overlapping Clustering with Sparseness Constraints
12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012), pp.486-494
International Conference on Data Mining Workshops
01/01/2012
DOI: 10.1109/ICDMW.2012.16
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.
Details
- Title: Subtitle
- Overlapping Clustering with Sparseness Constraints
- Creators
- Haibing Lu - Santa Clara UniversityYuan Hong - Rutgers, The State University of New JerseyW. Nick Street - University of IowaFei Wang - IBMHanghang Tong - Arizona State University
- Contributors
- J Vreeken (Editor)C Ling (Editor)M J Zaki (Editor)A Siebes (Editor)J X Yu (Editor)B Goethals (Editor)G Webb (Editor)Xindong Wu (Editor)
- Resource Type
- Conference proceeding
- Publication Details
- 12TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW 2012), pp.486-494
- Publisher
- IEEE
- Series
- International Conference on Data Mining Workshops
- DOI
- 10.1109/ICDMW.2012.16
- ISSN
- 2375-9232
- eISSN
- 2375-9259
- Number of pages
- 9
- Language
- English
- Date published
- 01/01/2012
- Academic Unit
- Nursing; Business Analytics; Computer Science; Bus Admin College
- Record Identifier
- 9984380528102771
Metrics
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