A New SOM-based R*-tree : Building and Retrieving

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  • Feng Yaokai
    Department of Intelligent Systems, Graduate School of Information Science and Electrical Engineering, Kyushu University : Doctral Program
  • Kubo Masaaki
    Department of Intelligent Systems, Graduate School of Information Science and Electrical Engineering, Kyushu University : Master's Program
  • Aghbari Zaher
    Department of Intelligent Systems, Faculty of Information Science and Electrical Engineering, Kyushu University
  • Makinouchi Akifumi
    Department of Intelligent Systems, Faculty of Information Science and Electrical Engineering, Kyushu University

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Other Title
  • 新しい自己組織化マップに基づくR*-tree : 構築と検索
  • アタラシイ ジコ ソシキカ マップ ニ モトヅク R tree コウチク ト ケンサク

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Abstract

R-trees are widely used in spatial and multi-dimensional databases. However, according to our investigation, the overlap among the leaf nodes of R-trees is serious and the objects are not well-clustered in the leaf nodes, which greatly affect the effect of the pruning strategies when nearest neighbor searching is performed and also affect the other search performance of R-trees. The forced reinsertion introduced in R*-tree can improve this problem to some extent, but can not completely solve this problem. In this study, we try to combine SUM (Self Organizing Map) technology and R*-tree technology to lessen the overlap among the leaf nodes of R*-tree and to improve the clustering degree of the objects in the leaf nodes. The experimental result shows that the SUM-based R*-tree proposed in this paper has a much better search performance than R*-tree.

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