Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
The complexity of non-hierarchical clustering with instance and cluster level constraints
Data Mining and Knowledge Discovery
Identifying and generating easy sets of constraints for clustering
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
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
Data Mining and Knowledge Discovery
A principled and flexible framework for finding alternative clusterings
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based semi-supervised clustering
Data Mining and Knowledge Discovery
Automated constraint selection for semi-supervised clustering algorithm
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Constraint selection for semi-supervised topological clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
On constrained spectral clustering and its applications
Data Mining and Knowledge Discovery
Multi-pitch Streaming of Harmonic Sound Mixtures
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Clustering with constraints is an emerging area of data mining research. However, most work assumes that the constraints are given as one large batch. In this paper we explore the situation where the constraints are incrementally given. In this way the user after seeing a clustering can provide positive and negative feedback via constraints to critique a clustering solution. We consider the problem of efficiently updating a clustering to satisfy the new and old constraints rather than reclustering the entire data set. We show that the problem of incremental clustering under constraints is NP-hard in general, but identify several sufficient conditions which lead to efficiently solvable versions. These translate into a set of rules on the types of constraints thatcan be added and constraint set properties that must be maintained. We demonstrate that this approach is more efficient than re-clustering the entire data set and has several other advantages.