Fast k-nearest neighbor classification using cluster-based trees
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.
metrics nonmetrics nearest neighbor classification cluster tree