Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Class Decomposition via Clustering: A New Framework for Low-Variance Classifiers
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Ant Clustering Using Ensembles of Partitions
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Using clustering to learn distance functions for supervised similarity assessment
Engineering Applications of Artificial Intelligence
Discovery of interesting regions in spatial data sets using supervised clustering
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering is applied to classified examples and has the goal of identifying class-uniform clusters that have a high probability density. This paper focuses on how data mining techniques in general, and classification techniques in particular, can benefit from knowledge obtained through supervised clustering. We discuss how better nearest neighbor classifiers can be constructed with the knowledge generated by supervised clustering, and provide experimental evidence that they are more efficient and more accurate than a traditional 1-nearest-neighbor classifier. Finally, we demonstrate how supervised clustering can be used to enhance simple classifiers.