Abstract
The SPY-TEC (Spherical Pyramid-Technique) [8] was proposed as a new indexing method for high-dimensional data spaces using a special partitioning strategy that divides a d-dimensional data space into 2d spherical pyramids. Although the authors of [8] proposed an efficient algorithm for processing hyperspherical range queries, they did not propose an algorithm for processing k-nearest neighbor queries that are frequently used in similarity search. In this paper, we propose an efficient algorithm for processing exact nearest neighbor queries on the SPY-TEC by extending the incremental nearest neighbor algorithm proposed in [10]. We also introduce a metric that can be used to guide an ordered best-first traversal when finding nearest neighbors on the SPYTEC. Finally, we show that our technique significantly outperforms the related techniques in processing k-nearest neighbor queries by comparing it to the R*-tree, the X-tree, and the sequential scan through extensive experiments.
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Lee, DH., Lee, HD., Choi, IH., Kim, HJ. (2001). An Algorithm for Incremental Nearest Neighbor Search in High-Dimensional Data Spaces. In: Kim, W., Ling, TW., Lee, YJ., Park, SS. (eds) The Human Society and the Internet Internet-Related Socio-Economic Issues. HSI 2001. Lecture Notes in Computer Science, vol 2105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47749-7_35
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DOI: https://doi.org/10.1007/3-540-47749-7_35
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