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Article: Reverse nearest neighbor search in metric spaces

TitleReverse nearest neighbor search in metric spaces
Authors
KeywordsMetric space
Reverse nearest neighbor
Issue Date2006
PublisherI E E E. The Journal's web site is located at http://www.computer.org/tkde
Citation
Ieee Transactions On Knowledge And Data Engineering, 2006, v. 18 n. 9, p. 1239-1252 How to Cite?
AbstractGiven a set D of objects, a reverse nearest neighbor (RNN) query returns the objects o in D such that o is closer to a query object g than to any other object in D, according to a certain similarity metric. The existing RNN solutions are not sufficient because they either 1) rely on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of "Euclidean objects" and similarity is measured using the L2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our techniques require no detailed representations of objects, and can be applied as long as their mutual distances can be computed and the distance metric satisfies the triangle inequality. We confirm the effectiveness of the proposed methods with extensive experiments. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/88955
ISSN
2015 Impact Factor: 2.476
2015 SCImago Journal Rankings: 2.087
References

 

DC FieldValueLanguage
dc.contributor.authorTao, Yen_HK
dc.contributor.authorYiu, MLen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-09-06T09:50:34Z-
dc.date.available2010-09-06T09:50:34Z-
dc.date.issued2006en_HK
dc.identifier.citationIeee Transactions On Knowledge And Data Engineering, 2006, v. 18 n. 9, p. 1239-1252en_HK
dc.identifier.issn1041-4347en_HK
dc.identifier.urihttp://hdl.handle.net/10722/88955-
dc.description.abstractGiven a set D of objects, a reverse nearest neighbor (RNN) query returns the objects o in D such that o is closer to a query object g than to any other object in D, according to a certain similarity metric. The existing RNN solutions are not sufficient because they either 1) rely on precomputed information that is expensive to maintain in the presence of updates or 2) are applicable only when the data consists of "Euclidean objects" and similarity is measured using the L2 norm. In this paper, we present the first algorithms for efficient RNN search in generic metric spaces. Our techniques require no detailed representations of objects, and can be applied as long as their mutual distances can be computed and the distance metric satisfies the triangle inequality. We confirm the effectiveness of the proposed methods with extensive experiments. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherI E E E. The Journal's web site is located at http://www.computer.org/tkdeen_HK
dc.relation.ispartofIEEE Transactions on Knowledge and Data Engineeringen_HK
dc.subjectMetric spaceen_HK
dc.subjectReverse nearest neighboren_HK
dc.titleReverse nearest neighbor search in metric spacesen_HK
dc.typeArticleen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TKDE.2006.148en_HK
dc.identifier.scopuseid_2-s2.0-33746874869en_HK
dc.identifier.hkuros122099en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33746874869&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue9en_HK
dc.identifier.spage1239en_HK
dc.identifier.epage1252en_HK
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridTao, Y=7402420191en_HK
dc.identifier.scopusauthoridYiu, ML=8589889600en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

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