A sample set condensation algorithm for the class sensitive artificial neural network
Pattern Recognition Letters
Reduction Techniques for Instance-BasedLearning Algorithms
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Fast k-Nearest Neighbor Classification Using Cluster-Based Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
Fast condensed nearest neighbor rule
ICML '05 Proceedings of the 22nd international conference on Machine learning
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Clustering-Based Reference Set Reduction for k-Nearest Neighbor
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
KEEL: a software tool to assess evolutionary algorithms for data mining problems
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BCI '09 Proceedings of the 2009 Fourth Balkan Conference in Informatics
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards efficient imputation by nearest-neighbors: a clustering-based approach
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The k-Nearest Neighbor (k-NN) classifier is a widely-used and effective classification method. The main k-NN drawback is that it involves high computational cost when applied on large datasets. Many Data Reduction Techniques have been proposed in order to speed-up the classification process. However, their effectiveness depends on the level of noise in the data. This paper shows that the k-means clustering algorithm can be used as a noise-tolerant Data Reduction Technique. The conducted experimental study illustrates that if the reduced dataset includes the k-means centroids as representatives of the initial data, performance is not negatively affected as much by the addition of noise.