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<p><b>Abstract</b>—The SPY-TEC (Spherical Pyramid-Technique) was proposed as a new indexing method for high-dimensional data spaces using a special partitioning strategy that divides a <it>d</it>-dimensional data space into <tmath>2d</tmath> spherical pyramids. In the SPY-TEC, an efficient algorithm for processing hyperspherical range queries was introduced with a special partitioning strategy. However, the technique for processing <it>k</it>-nearest-neighbor queries, which are frequently used in similarity search, was not proposed. In this paper, we propose an efficient algorithm for processing nearest-neighbor queries on the SPY-TEC by extending the incremental nearest-neighbor algorithm. We also introduce a metric that can be used to guide an ordered best-first traversal when finding nearest neighbors on the SPY-TEC. Finally, we show that our technique significantly outperforms the related techniques in processing <it>k</it>-nearest-neighbor queries by comparing it to the R*-tree, the X-tree, and the sequential scan through extensive experiments.</p>
Similarity search, high-dimensional index technique, nearest-neighbor query, incremental nearest-neighbor algorithm, SPY-TEC.

H. Kim and D. Lee, "An Efficient Technique for Nearest-Neighbor Query Processing on the SPY-TEC," in IEEE Transactions on Knowledge & Data Engineering, vol. 15, no. , pp. 1472-1486, 2003.
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