Dimensional testing for reverse k-nearest neighbor search

Guillaume Casanova, Elias Englmeier, Michael E. Houle, Peer Kröger, Michael Nett, Erich Schubert, Arthur Zimek

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence that our method significantly outperforms its competitors in terms of the tradeoff between execution time and the quality of the approximation. Our approach thus addresses many of the scalability issues surrounding the use of previous methods in data mining.
Original languageEnglish
JournalProceedings of the VLDB Endowment
Volume10
Issue number7
Pages (from-to)769-780
ISSN2150-8097
DOIs
Publication statusPublished - 2017
Event43rd International Conference on Very Large Data Bases - Technical University of Munich, Munich, Germany
Duration: 28 Aug 20171 Sep 2017
Conference number: 43

Conference

Conference43rd International Conference on Very Large Data Bases
Number43
LocationTechnical University of Munich
CountryGermany
CityMunich
Period28/08/201701/09/2017

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Testing
Data mining
Scalability
Costs
Nearest neighbor search

Cite this

Casanova, G., Englmeier, E., Houle, M. E., Kröger, P., Nett, M., Schubert, E., & Zimek, A. (2017). Dimensional testing for reverse k-nearest neighbor search. Proceedings of the VLDB Endowment, 10(7), 769-780. https://doi.org/10.14778/3067421.3067426
Casanova, Guillaume ; Englmeier, Elias ; Houle, Michael E. ; Kröger, Peer ; Nett, Michael ; Schubert, Erich ; Zimek, Arthur. / Dimensional testing for reverse k-nearest neighbor search. In: Proceedings of the VLDB Endowment. 2017 ; Vol. 10, No. 7. pp. 769-780.
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title = "Dimensional testing for reverse k-nearest neighbor search",
abstract = "Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence that our method significantly outperforms its competitors in terms of the tradeoff between execution time and the quality of the approximation. Our approach thus addresses many of the scalability issues surrounding the use of previous methods in data mining.",
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Casanova, G, Englmeier, E, Houle, ME, Kröger, P, Nett, M, Schubert, E & Zimek, A 2017, 'Dimensional testing for reverse k-nearest neighbor search' Proceedings of the VLDB Endowment, vol. 10, no. 7, pp. 769-780. https://doi.org/10.14778/3067421.3067426

Dimensional testing for reverse k-nearest neighbor search. / Casanova, Guillaume; Englmeier, Elias; Houle, Michael E.; Kröger, Peer; Nett, Michael; Schubert, Erich; Zimek, Arthur.

In: Proceedings of the VLDB Endowment, Vol. 10, No. 7, 2017, p. 769-780.

Research output: Contribution to journalConference articleResearchpeer-review

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AB - Given a query object q, reverse k-nearest neighbor (RkNN) search aims to locate those objects of the database that have q among their k-nearest neighbors. In this paper, we propose an approximation method for solving RkNN queries, where the pruning operations and termination tests are guided by a characterization of the intrinsic dimensionality of the data. The method can accommodate any index structure supporting incremental (forward) nearest-neighbor search for the generation and verification of candidates, while avoiding impractically-high preprocessing costs. We also provide experimental evidence that our method significantly outperforms its competitors in terms of the tradeoff between execution time and the quality of the approximation. Our approach thus addresses many of the scalability issues surrounding the use of previous methods in data mining.

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Casanova G, Englmeier E, Houle ME, Kröger P, Nett M, Schubert E et al. Dimensional testing for reverse k-nearest neighbor search. Proceedings of the VLDB Endowment. 2017;10(7):769-780. https://doi.org/10.14778/3067421.3067426