K-tree: a height balanced tree structured vector quantizer

Geva, Shlomo (2000) K-tree: a height balanced tree structured vector quantizer. In Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop, IEEE, Sydney, pp. 271-280.

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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: 16976
Item Type: Conference Paper
Refereed: Yes
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
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: 13 Jul 2011 22:19
Last Modified: 14 Jul 2011 10:51

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