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Improving clustering with constrained communities
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DOI：10.1016/j.neucom.2014.09.106.png)
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摘要
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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 substructures 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. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Constrained clustering
Constrained community
Transductive learning
Community discovery
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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