Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A spectral method to separate disconnected and nearly-disconnected web graph components
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
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
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Value, cost, and sharing: open issues in constrained clustering
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Boosting Clustering by Active Constraint Selection
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Improving constrained clustering with active query selection
Pattern Recognition
Active spectral clustering via iterative uncertainty reduction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Active selection of clustering constraints: a sequential approach
Pattern Recognition
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This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) derived from a similarity matrix. The ACCESS method uses an analysis based on the theoretical properties of spectral decomposition to identify data items that are likely to be located on the boundaries of clusters, and for which providing constraints can resolve ambiguity in the cluster descriptions. Empirical results on three synthetic and five real data sets show that ACCESS significantly outperforms constrained spectral clustering using randomly selected constraints.