Composite Distance Transformation for Indexing and k -Nearest-Neighbor Searching in High-Dimensional Spaces
Composite Distance Transformation for Indexing and k-Nearest-Neighbor Searching in High-Dimensional Spaces

Due to the famous dimensionality curse problem, search in a high-dimensional space is considered as a “hard” problem. In this paper, a novel composite distance transformation method, which is called CDT, is proposed to support a fast k-nearest-neighbor (k-NN) search in high-dimensional spaces. In CDT, all (n) data points are first grouped into some clusters by a k-Means clustering algorithm. Then a composite distance key of each data point is computed. Finally, these index keys of such n data points are inserted by a partition-based B+-tree. Thus, given a query point, its k-NN search in high-dimensional spaces is transformed into the search in the single dimensional space with the aid of CDT index. Extensive performance studies are conducted to evaluate the effectiveness and efficiency of the proposed scheme. Our results show that this method outperforms the state-of-the-art high-dimensional search techniques, such as the X-Tree, VA-file, iDistance and NB-Tree. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users. Partially supported by the National Natural Science Foundation of China (Grant No. 60533090), National Science Fund for Distinguished Young Scholars (Grant No. 60525108), the National Grand Fundamental Research 973 Program of China (Grant No. 2002CB312101), Science and Technology Project of Zhejiang Province (Grant Nos. 2005C13032, 2005C11001-05) and China-America Academic Digital Library Project (see ).