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Partition Level Constrained Clustering.

Authors
  • Liu, Hongfu1
  • Tao, Zhiqiang2
  • Fu, Yun3
  • 1 ECE, Northeastern University, Somerville, Massachusetts United States 02145 (e-mail: [email protected]). , (United States)
  • 2 ECE, Northeastern University, Somerville, Massachusetts United States (e-mail: [email protected]). , (United States)
  • 3 ECE, Northeastern University, Boston, Massachusetts United States (e-mail: [email protected]). , (United States)
Type
Published Article
Journal
IEEE transactions on pattern analysis and machine intelligence
Publication Date
Oct 16, 2017
Pages
1–1
Identifiers
DOI: 10.1109/TPAMI.2017.2763945
PMID: 29053445
Source
Medline
Keywords
License
Unknown

Abstract

Constrained clustering uses pre-given knowledge to improve the clustering performance. Here we use a new constraint called partition level side information and propose the Partition Level Constrained Clustering (PLCC) framework' where only a small proportion of the data is given labels to guide the procedure of clustering. Our goal is to find a partition which captures the intrinsic structure from the data itself, and also agrees with the partition level side information. Then we derive the algorithm of partition level side information based on K-means and give its corresponding solution. Further, we extend it to handle multiple side information and design the algorithm of partition level side information for spectral clustering. Extensive experiments demonstrate the effectiveness and efficiency of our method compared to pairwise constrained clustering and ensemble clustering methods, even in the inconsistent cluster number setting, which verifies the superiority of partition level side information to pairwise constraints. Besides, our method has high robustness to noisy side information and moreover we validate the performance of our method with multiple side information. Finally, the image cosegmentation application based on saliency-guided side information demonstrates the effectiveness of PLCC as a flexible framework in different domains, even with the unsupervised side information.Constrained clustering uses pre-given knowledge to improve the clustering performance. Here we use a new constraint called partition level side information and propose the Partition Level Constrained Clustering (PLCC) framework' where only a small proportion of the data is given labels to guide the procedure of clustering. Our goal is to find a partition which captures the intrinsic structure from the data itself, and also agrees with the partition level side information. Then we derive the algorithm of partition level side information based on K-means and give its corresponding solution. Further, we extend it to handle multiple side information and design the algorithm of partition level side information for spectral clustering. Extensive experiments demonstrate the effectiveness and efficiency of our method compared to pairwise constrained clustering and ensemble clustering methods, even in the inconsistent cluster number setting, which verifies the superiority of partition level side information to pairwise constraints. Besides, our method has high robustness to noisy side information and moreover we validate the performance of our method with multiple side information. Finally, the image cosegmentation application based on saliency-guided side information demonstrates the effectiveness of PLCC as a flexible framework in different domains, even with the unsupervised side information.

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