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K-d decision tree: an accelerated and memory efficient nearest neighbor classifier

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3 Author(s)
Shibata, T. ; Fac. of Syst. Eng., Wakayama Univ., Japan ; Kato, T. ; Wada, T.

Most nearest neighbor (NN) classifiers employ NN search algorithms for the acceleration. However, NN classification does not always require the NN search. Based on this idea, we propose a novel algorithm named k-d decision tree (KDDT). Since KDDT uses Voronoi condensed prototypes, it is less memory consuming than naive NN classifiers. We have confirmed that KDDT is much faster than NN search based classifiers through the comparative experiment (from 9 to 369 times faster).

Published in:

Data Mining, 2003. ICDM 2003. Third IEEE International Conference on

Date of Conference:

19-22 Nov. 2003