All Near Neighbor GraphWithout Searching

Autores
Chávez, Edgar; Ludueña, Verónica; Reyes, Nora Susana; Kasián, Fernando
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.
Facultad de Informática
Materia
Ciencias Informáticas
near neighbor graph
proximity search
clustering
metric indexing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/66742
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/66742
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling All Near Neighbor GraphWithout SearchingChávez, EdgarLudueña, VerónicaReyes, Nora SusanaKasián, FernandoCiencias Informáticasnear neighbor graphproximity searchclusteringmetric indexingGiven a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.Facultad de Informática2018-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf61-67http://sedici.unlp.edu.ar/handle/10915/66742enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/695/225info:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e07info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:10:01Zoai:sedici.unlp.edu.ar:10915/66742Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:10:01.438SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv All Near Neighbor GraphWithout Searching
title All Near Neighbor GraphWithout Searching
spellingShingle All Near Neighbor GraphWithout Searching
Chávez, Edgar
Ciencias Informáticas
near neighbor graph
proximity search
clustering
metric indexing
title_short All Near Neighbor GraphWithout Searching
title_full All Near Neighbor GraphWithout Searching
title_fullStr All Near Neighbor GraphWithout Searching
title_full_unstemmed All Near Neighbor GraphWithout Searching
title_sort All Near Neighbor GraphWithout Searching
dc.creator.none.fl_str_mv Chávez, Edgar
Ludueña, Verónica
Reyes, Nora Susana
Kasián, Fernando
author Chávez, Edgar
author_facet Chávez, Edgar
Ludueña, Verónica
Reyes, Nora Susana
Kasián, Fernando
author_role author
author2 Ludueña, Verónica
Reyes, Nora Susana
Kasián, Fernando
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
near neighbor graph
proximity search
clustering
metric indexing
topic Ciencias Informáticas
near neighbor graph
proximity search
clustering
metric indexing
dc.description.none.fl_txt_mv Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.
Facultad de Informática
description Given a collection of n objects equipped with a distance function d(·, ·), the Nearest Neighbor Graph (NNG) consists in finding the nearest neighbor of each object in the collection. Without an index the total cost of NNG is quadratic. Using an index the cost would be sub-quadratic if the search for individual items is sublinear. Unfortunately, due to the so called curse of dimensionality the indexed and the brute force methods are almost equally inefficient. In this paper we present an efficient algorithm to build the Near Neighbor Graph (nNG), that is an approximation of NNG, using only the index construction, without actually searching for objects.
publishDate 2018
dc.date.none.fl_str_mv 2018-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/66742
url http://sedici.unlp.edu.ar/handle/10915/66742
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/JCST/article/view/695/225
info:eu-repo/semantics/altIdentifier/issn/1666-6038
info:eu-repo/semantics/altIdentifier/doi/10.24215/16666038.18.e07
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/4.0/
Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.format.none.fl_str_mv application/pdf
61-67
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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