Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Error bounds for correlation clustering
ICML '05 Proceedings of the 22nd international conference on Machine learning
Cumulative Voting Consensus Method for Partitions with Variable Number of Clusters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Data Clustering: User's Dilemma
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
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
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Constrained clustering has received substantial attention recently. This framework proposes to support the clustering process by prior knowledge in terms of constraints (on data items, cluster size, etc.). In this work we introduce clustering combination into the constrained clustering framework. It is argued that even if all clusterings of an ensemble satisfy the constraints, there is still a need of carefully considering the constraints in the combination method in order to avoid a violation in the final combined clustering. We propose an evidence accumulation approach for this purpose, which is quantitatively compared with constrained algorithms and unconstrained combination methods.