Efficiency of a Good But Not Linear Set Union Algorithm
Journal of the ACM (JACM)
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
Personalized Hierarchical Clustering
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
A Perspective on Cluster Analysis
Statistical Analysis and Data Mining
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Data Mining and Knowledge Discovery
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
On the effects of constraints in semi-supervised hierarchical clustering
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Constrained clustering using SAT
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
Efficient hierarchical clustering of large high dimensional datasets
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
The area of constrained clustering has been actively pursued for the last decade. A more recent extension that will be the focus of this paper is constrained hierarchical clustering which allows building user-constrained dendrograms/trees. Like all forms of constrained clustering, previous work on hierarchical constrained clustering uses simple constraints that are typically implemented in a procedural language. However, there exists mature results and packages in the fields of constraint satisfaction languages and solvers that the constrained clustering field has yet to explore. This work marks the first steps towards introducing constraints satisfaction languages/solvers into hierarchical constrained clustering. We make several significant contributions. We show how many existing and new constraints for hierarchical clustering, can be modeled as a Horn-SAT problem that is easily solvable in polynomial time and which allows their implementation in any number of declarative languages or efficient solvers. We implement our own solver for efficiency reasons. We then show how to formulate constrained hierarchical clustering in a flexible manner so that any number of algorithms, whose output is a dendrogram, can make use of the constraints.