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SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
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SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
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SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
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ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
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PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Semi-Supervised Learning
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Data Mining and Knowledge Discovery
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Selecting correct dimensions is very important to subspace clustering and is a challenging issue. This paper studies semi-supervised approach to the problem. In this setting, limited domain knowledge in the form of space level pair-wise constraints, i.e., must-links and cannot-links, are available. We propose a semi-supervised subspace clustering (S^3C) algorithm that exploits constraint inconsistence for dimension selection. Our algorithm firstly correlates globally inconsistent constraints to dimensions in which they are consistent, then unites constraints with common correlating dimensions, and finally forms the subspaces according to the constraint unions. Experimental results show that S^3C is superior to the typical unsupervised subspace clustering algorithm FINDIT, and the other constraint based semi-supervised subspace clustering algorithm SC-MINER.