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Fast k-nearest neighbor classification using cluster-based trees

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2 Author(s)
Bin Zhang ; Dept. of Human Genetics & Biostat., UCLA, Los Angeles, CA, USA ; Srihari, S.N.

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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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:26 ,  Issue: 4 )