Computational geometry: an introduction
Computational geometry: an introduction
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Self-Organizing Maps
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Using Representative-Based Clustering for Nearest Neighbor Dataset Editing
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Supervised selection of dynamic features, with an application to telecommunication data preparation
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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Since its introduction, the nearest neighbor rule has been widely refined and there exists many techniques for prototypes selection or construction. The underlying structure of such rules is the Voronoi partition induced by the prototypes. Construction of the best Voronoi partition often relies on the generalisation performance and thus faces the risk of overfitting the data.In this paper, we adopt a descriptive approach for the supervised evaluation of medoid-based Voronoi partitions. The resulting criterion measures the discrimination of the classes, is parameter free and prevents from overfitting. Experiments on real and synthetic datasets illustrate these properties. Although this criterion is not related to the classifying task, the accuracy and robustness of the induced classifier are also compared with standard methods, such as the nearest neighbor rule and the linear vector quantization method.