Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Semi-supervised protein classification using cluster kernels
Bioinformatics
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
Semi-supervised clustering ensemble based on multi-ant colonies algorithm
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Linear semi-supervised projection clustering by transferred centroid regularization
Journal of Intelligent Information Systems
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A number of clustering algorithms have been proposed for use in tasks where a limited degree of supervision is available. This prior knowledge is frequently provided in the form of pairwise must-link and cannot-link constraints. While the incorporation of pairwise supervision has the potential to improve clustering accuracy, the composition and cardinality of the constraint sets can significantly impact upon the level of improvement. We demonstrate that it is often possible to correctly "guess" a large number of constraints without supervision from the co-associations between pairs of objects in an ensemble of clusterings. Along the same lines, we establish that constraints based on pairs with uncertain co-associations are particularly informative, if known. An evaluation on text data shows that this provides an effective criterion for identifying constraints, leading to a reduction in the level of supervision required to direct a clustering algorithm to an accurate solution.