K-tree : a height balanced tree structured vector quantizer

Geva, Shlomo (2000) K-tree : a height balanced tree structured vector quantizer. In NNSP-2000, IEEE Neural Network for Signal Processing Workshop 2000, 11-13 December 2000, Sydney.


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We describe a clustering algorithm for the design of height balanced trees for vector quantisation. The algorithm is a hybrid of the B-tree and the k-means clustering procedure. K-tree supports on-line dynamic tree construction. The properties of the resulting search tree and clustering codebook are comparable to that of codebooks obtained by TSVQ, the commonly used recursive k-means algorithm for constructing vector quantization search trees. The K-tree algorithm scales up to larger data sets than TSVQ, produces codebooks with somewhat higher distortion rates, but facilitates greater control over the properties of the resulting codebooks. We demonstrate the properties and performance of K-tree and compare it with TSVQ and with k-means.

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ID Code: 16970
Item Type: Conference Item (Other)
Refereed: Yes
Additional Information: Author selected Creative Commons Attributes on the license field
Keywords: clustering, tree, hierarchical clustering, k-means, c-means
DOI: 10.1109/NNSP.2000.889418
ISBN: 0780362780
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Software Engineering & Data Communications
Copyright Owner: Copyright 2000 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 16 Dec 2008 22:34
Last Modified: 13 Jul 2011 22:19

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