Generating hard satisfiability problems
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
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
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Intelligent clustering with instance-level constraints
Intelligent clustering with instance-level constraints
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Towards constrained co-clustering in ordered 0/1 data sets
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Measuring constraint-set utility for partitional clustering algorithms
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Active constrained clustering by examining spectral eigenvectors
DS'05 Proceedings of the 8th international conference on Discovery Science
Collaborative clustering with background knowledge
Data & Knowledge Engineering
Bagging Constraint Score for feature selection with pairwise constraints
Pattern Recognition
Analysis of time series data with predictive clustering trees
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
Background knowledge integration in clustering using purity indexes
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
A new contiguity-constrained agglomerative hierarchical clustering algorithm for image segmentation
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Improving constrained clustering with active query selection
Pattern Recognition
Active selection of clustering constraints: a sequential approach
Pattern Recognition
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Clustering is an important tool for data mining, since it can identify major patterns or trends without any supervision (labeled data). Over the past five years, semi-supervised (constrained) clustering methods have become very popular. These methods began with incorporating pairwise constraints and have developed into more general methods that can learn appropriate distance metrics. However, several important open questions have arisen about which constraints are most useful, how they can be actively acquired, and when and how they should be propagated to neighboring points. This position paper describes these open questions and suggests future directions for constrained clustering research.