Indexing high-dimensional data in dual distance spaces: a symmetrical encoding approach

  • Authors:

  • Yi Zhuang;Yueting Zhuang;Qing Li;Lei Chen;Yi Yu

  • Affiliations:

  • Zhejiang University, P.R.China;Zhejiang University, P.R.China;City University of Hong Kong, HKSAR, P.R.China;Hong Kong University of Science and Technology, HKSAR, P.R.China;Nara Women's University, Japan

  • Venue:
  • EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
  • Year:
  • 2008

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Abstract

Due to the well-known dimensionality curse problem, search in a high-dimensional space is considered as a "hard" problem. In this paper, a novel symmetrical encoding-based index structure, which is called EHD-Tree (for symmetrical Encoding-based Hybrid Distance Tree), is proposed to support fast k-Nearest-Neighbor (k-NN) search in high-dimensional spaces. In an EHD-Tree, all data points are first grouped into clusters by a k-Means clustering algorithm. Then the uniform ID number of each data point is obtained by a dual-distance-driven encoding scheme in which each cluster sphere is partitioned twice according to the dual distances of start- and centroid-distance. Finally, the uniform ID number and the centroid-distance of each data point are combined to get a uniform index key, the latter is then indexed through a partition-based B+-tree. Thus, given a query point, its k-NN search in high-dimensional spaces can be transformed into search in a single dimensional space with the aid of the EHD-Tree index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of our proposed scheme, and the results demonstrate that this method outperforms the state-of-the-art high dimensional search techniques such as the X-Tree, VA-file, iDistance and NB-Tree, especially when the query radius is not very large.