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<p><b>Abstract</b>—Most fast <tmath>k{\hbox{-}}{\rm{nearest}}</tmath> neighbor (<tmath>k{\hbox{-}}{\rm{NN}}</tmath>) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate <tmath>k{\hbox{-}}{\rm{NN}}</tmath> classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.</p>
Nearest neighbor classification, nonmetrics, metrics, cluster tree.

B. Zhang and S. N. Srihari, "Fast k-Nearest Neighbor Classification Using Cluster-Based Trees," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 26, no. , pp. 525-528, 2004.
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