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Fast k-nearest neighbor classification using cluster-based trees.

Bin Zhang·Sargur N Srihari
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Abstract

Most fast k-nearest neighbor (k-NN) 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 k-NN 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.

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Bin Zhang, Sargur N Srihari. 2004. Fast k-nearest neighbor classification using cluster-based trees.. https://doi.org/10.1109/tpami.2004.1265868

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