Presentation 2001/7/11
NSBR^*-tree : Building and Retrieving
Yaokai FENG, Masaaki KUBO, zaher Aghbari, Akifumi MAKINOUCHI,
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Abstract(in English) R-trees are a common indexing technique for multi-dimensional data and are widely used in spatial and multi-dimensional databases. Nearest neighbor search(called NN search)is very popular in multimedia database and spatial database. According to our investigation, for a given database, the degree of the leaf nodes clustering the objects is a great factor on the NN searching performance. For R-trees, the objects are not well-clustered by its leaf nodes. Some packing algorithms for R-trees have been proposed. However, in these packing algorithms, the distribution of objects in its leaf nodes may not reflect the actual situation of objects and can not lead to a good clustering. An attempt combining clustering technology and R-trees(called SOM-based R^*-tree)is proposed by K.Oh and Y.Feng et al., which tries to decrease the number of objects in R-trees by building R-trees using the representative feature vectors of clusters instead of objects themselves. In the present paper, a new structure called NSBR^*-tree is proposed. The experimental result shows that the NSBR^*-tree has a much better searching performance.
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Paper # DE2001-63
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Committee DE
Conference Date 2001/7/11(1days)
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Registration To Data Engineering (DE)
Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) NSBR^*-tree : Building and Retrieving
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1st Author's Name Yaokai FENG
1st Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University()
2nd Author's Name Masaaki KUBO
2nd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
3rd Author's Name zaher Aghbari
3rd Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
4th Author's Name Akifumi MAKINOUCHI
4th Author's Affiliation Graduate School of Information Science and Electrical Engineering, Kyushu University
Date 2001/7/11
Paper # DE2001-63
Volume (vol) vol.101
Number (no) 192
Page pp.pp.-
#Pages 8
Date of Issue