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

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 partitionbased B+ -tree. Thus, given a query point, its k-NN search in highdimensional spaces can be transformed into search in a single dimensional space with the...

• Type: Conference
• Year: 2008
• Where: EDBT
• Authors: Yi Zhuang, Yueting Zhuang, Qing Li, Lei Chen 0002, Yi Yu
• Keywords: Database, Dimensional Search Techniques, EDBT 2008, Uniform Id Number, Uniform Index Key