C4.5: programs for machine learning
C4.5: programs for machine learning
ACM Computing Surveys (CSUR)
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
Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Theoretical Comparison between the Gini Index and Information Gain Criteria
Annals of Mathematics and Artificial Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating constraints and metric learning in semi-supervised clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Beam search induction and similarity constraints for predictive clustering trees
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
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
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Constraint based induction of multi-objective regression trees
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Decision trees for hierarchical multi-label classification
Machine Learning
On context-aware co-clustering with metadata support
Journal of Intelligent Information Systems
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Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on the set of clusters. We consider instance level constraints, such as must-link and cannot-link. This type of constraints has been successfully used in popular clustering algorithms, such as k-means and hierarchical agglomerative clustering. This paper shows how clustering trees can support instance level constraints. Clustering trees are decision trees that partition the instances into homogeneous clusters. Clustering trees provide a symbolic description for each cluster. To handle non-trivial constraint sets, we extend clustering trees to support disjunctive descriptions. The paper's main contribution is ClusILC, an efficient algorithm for building such trees. We present experiments comparing ClusILC to COP-k-means.