COMPARATIVE STUDY
JOURNAL ARTICLE

Fast k-nearest neighbor classification using cluster-based trees

Bin Zhang, Sargur N Srihari
IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 26 (4): 525-8
15382657
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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