Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An interactive clustering-based approach to integrating source query interfaces on the deep Web
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Online and batch learning of pseudo-metrics
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Learning nonparametric kernel matrices from pairwise constraints
Proceedings of the 24th international conference on Machine learning
Enhancing semi-supervised clustering: a feature projection perspective
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
Active selection of clustering constraints: a sequential approach
Pattern Recognition
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This paper proposes a method of learning a similarity matrix from pairwise constraints for interactive clustering. The similarity matrix can be learned by solving an optimization problem as semi-definite programming where we give additional constraints about neighbors of constrained pairwise data besides original constraints. For interactive clustering, since we can get only a few pairwise constraints from a user, we need to extend such constraints to richer ones. Thus this proposed method to extend the pairwise constraints to space-level ones is effective to interactive clustering. First we formalize clustering with constrained similarity learning, and then introduce the extended constraints as linear constraints. We verify the effectiveness of our proposed method by applying it on a simple clustering task. The results of the experiments shows that our method is promising.