K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier

Melbourne, Florida November 19-November 22

DOI Bookmark:

http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250997

Third IEEE International Conference o ...

	This Article
	PURCHASE ARTICLE: $19 PDF HTML IEEE Xplore Subscribers
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan/Endnote
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Tomoyuki Shibata Articles by Takekazu Kato Articles by Toshikazu Wada

	ASCII Text	x
	Tomoyuki Shibata, Takekazu Kato, Toshikazu Wada, "K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier," Data Mining, IEEE International Conference on, pp. 641, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250997, author = {Tomoyuki Shibata and Takekazu Kato and Toshikazu Wada}, title = {K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {641}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250997}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier SN - 0-7695-1978-4 SP EP A1 - Tomoyuki Shibata, A1 - Takekazu Kato, A1 - Toshikazu Wada, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Tomoyuki Shibata, Wakayama University

Takekazu Kato, Wakayama University

Toshikazu Wada, Wakayama University

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).

Citation:

Tomoyuki Shibata, Takekazu Kato, Toshikazu Wada, "K-D Decision Tree: An Accelerated and Memory Efficient Nearest Neighbor Classifier," icdm, pp.641, Third IEEE International Conference on Data Mining (ICDM'03), 2003

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.