A new indexing method for high dimensional dataset

An, Jiyuan, Chen, Yi-Ping Phoebe, Xu, Qinying and Zhou, Xiaofang 2005, A new indexing method for high dimensional dataset, Lecture notes in computer science, vol. 3453, pp. 385-397.

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Title A new indexing method for high dimensional dataset
Author(s) An, Jiyuan
Chen, Yi-Ping Phoebe
Xu, Qinying
Zhou, Xiaofang
Journal name Lecture notes in computer science
Volume number 3453
Start page 385
End page 397
Publisher Springer-Verlag
Place of publication Berlin , Germany
Publication date 2005
ISSN 0302-9743
Keyword(s) computer science
theory & methods
Summary Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©Springer-Verlag Berlin Heidelberg, 2005
Persistent URL http://hdl.handle.net/10536/DRO/DU:30003066

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