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Improving clustering with constrained communities.
- Source :
-
Neurocomputing . May2016, Vol. 188, p239-252. 14p. - Publication Year :
- 2016
-
Abstract
- In this paper, we propose a new constrained clustering algorithm, named Constrained Community Clustering (C 3 ). It can utilize both must-link and cannot-link constraints that specify the pairs of data that belong to the same or different clusters. Instead of directly enforcing the pairwise constraints on the constrained data, C 3 first builds constrained communities around each constrained data, and then exert pairwise constraints on the constrained communities. Therefore, C 3 can not only extend the influence of pairwise constraints to the surrounding unconstrained data, but also uncover the underlying sub-structures of the clusters. The promising experimental results on the real-world text documents, handwritten digits, alphabetic characters, face recognition, and community discovery illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 188
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 114313283
- Full Text :
- https://doi.org/10.1016/j.neucom.2014.09.106