Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth 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
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Constrained Clustering: Advances in Algorithms, Theory, and Applications
Clustering
Effective semi-supervised document clustering via active learning with instance-level constraints
Knowledge and Information Systems
Structural Segmentation of Musical Audio by Constrained Clustering
IEEE Transactions on Audio, Speech, and Language Processing
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Constrained clustering addresses the problem of creating minimum variance clusters with the added complexity that there is a set of constraints that must be fulfilled by the elements in the cluster. Research in this area has focused on “must-link” and “cannot-link” constraints, in which pairs of elements must be in the same or in different clusters, respectively. In this work we present a heuristic procedure to perform clustering in two classes when the restrictions affect all the elements of the two clusters in such a way that they depend on the elements present in the cluster. This problem is highly susceptible to outliers in each cluster (extreme values that create infeasible solutions), so the procedure eliminates elements with extreme values in both clusters, and achieves adequate performance measures at the same time. The experiments performed on a company database allow to discover a great deal of information, with results that are more readily interpretable when compared to classical k-means clustering.