Publikation: What is the nearest neighbor in high dimensional spaces?
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also to the quality issue. In this paper, we discuss the quality issue and identify a new generalized notion of nearest neighbor search as the relevant problem in high dimensional space. In contrast to previous approaches, our new notion of nearest neighbor search does not treat all dimensions equally but uses a quality criterion to select relevant dimensions (projections) with respect to the given query. As an example for a useful quality criterion, we rate how well the data is clustered around the query point within the selected projection. We then propose an efficient and effective algorithm to solve the generalized nearest neighbor problem. Our experiments based on a number of real and synthetic data sets show that our new approach provides new insights into the nature of nearest neighbor search on high dimensional data.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
HINNEBURG, Alexander, Charu C. AGGARWAL, Daniel A. KEIM, 2000. What is the nearest neighbor in high dimensional spaces?. 26th Internat. Conference on Very Large Databases. Cairo, Egypt, 2000. In: Proc. of the 26th Internat. Conference on Very Large Databases, Cairo, Egypt, 2000. 2000, pp. 506-515BibTex
@inproceedings{Hinneburg2000neare-5849, year={2000}, title={What is the nearest neighbor in high dimensional spaces?}, booktitle={Proc. of the 26th Internat. Conference on Very Large Databases, Cairo, Egypt, 2000}, pages={506--515}, author={Hinneburg, Alexander and Aggarwal, Charu C. and Keim, Daniel A.} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/5849"> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5849/1/P506.pdf"/> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/> <dcterms:title>What is the nearest neighbor in high dimensional spaces?</dcterms:title> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:format>application/pdf</dc:format> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:39Z</dc:date> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5849"/> <dc:contributor>Aggarwal, Charu C.</dc:contributor> <dcterms:issued>2000</dcterms:issued> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:creator>Hinneburg, Alexander</dc:creator> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5849/1/P506.pdf"/> <dcterms:bibliographicCitation>First publ. in: Proc. of the 26th Internat. Conference on Very Large Databases, Cairo, Egypt, 2000, pp. 506-515</dcterms:bibliographicCitation> <dc:creator>Aggarwal, Charu C.</dc:creator> <dcterms:abstract xml:lang="eng">Nearest neighbor search in high dimensional spaces is an interesting and important problem which is relevant for a wide variety of novel database applications. As recent results show, however, the problem is a very difficult one, not only with regards to the performance issue but also to the quality issue. In this paper, we discuss the quality issue and identify a new generalized notion of nearest neighbor search as the relevant problem in high dimensional space. In contrast to previous approaches, our new notion of nearest neighbor search does not treat all dimensions equally but uses a quality criterion to select relevant dimensions (projections) with respect to the given query. As an example for a useful quality criterion, we rate how well the data is clustered around the query point within the selected projection. We then propose an efficient and effective algorithm to solve the generalized nearest neighbor problem. Our experiments based on a number of real and synthetic data sets show that our new approach provides new insights into the nature of nearest neighbor search on high dimensional data.</dcterms:abstract> <dc:contributor>Hinneburg, Alexander</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:39Z</dcterms:available> <dc:language>eng</dc:language> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> </rdf:Description> </rdf:RDF>