Abstract
Many applications require to determine the k-nearest neighbors for multiple query points simultaneously. This task is known as all-(k)-nearest- neighbor (AkNN) query. In this paper, we suggest a new method for efficient AkNN query processing which is based on spherical approximations for indexing and query set representation. In this setting, we propose trigonometric pruning which enables a significant decrease of the remaining search space for a query. Employing this new pruning method, we considerably speed up AkNN queries. © 2010 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Emrich, T., Graf, F., Kriegel, H. P., Schubert, M., & Thoma, M. (2010). Optimizing all-nearest-neighbor queries with trigonometric pruning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6187 LNCS, pp. 501–518). https://doi.org/10.1007/978-3-642-13818-8_35
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