We propose an original solution for the general reverse k-nearest neighbor (RkNN) search problem in Euclidean spaces. Compared to the limitations of existing methods for the RkNN search, our approach works on top of Multi-Resolution Aggregate (MRA) versions of any index structures for multidimensional feature spaces where each non-leaf node is additionally associated with aggregate information like the sum of all leaf-entries indexed by that node. Our solution outperforms the state-of-the-art RkNN algorithms in terms of query execution times because it exploits advanced strategies for pruning index entries. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Kriegel, H. P., Kröger, P., Renz, M., Züfle, A., & Katzdobler, A. (2009). Reverse k-nearest neighbor search based on aggregate point access methods. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5566 LNCS, pp. 444–460). https://doi.org/10.1007/978-3-642-02279-1_32
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