Effective nearest neighbor indexing with the euclidean metric

The nearest neighbor search is an important operation widely-used in multimedia databases. In higher dimensions, most of previous methods for nearest neighbor search become inefficient and require to compute nearest neighbor distances to a large fraction of points in the space. In this paper, we present a new approach for processing nearest neighbor search with the Euclidean metric, which searches over only a small subset of the original space. This approach effectively approximates clusters by encapsulating them into geometrically regular shapes and also computes better upper and lower bounds of the distances from the query point to the clusters. For showing the effectiveness of the proposed approach, we perform extensive experiments. The results reveal that the proposed approach significantly outperforms the X-tree as well as the sequential scan.