Comparative Evaluation of Region Query Strategies for DBSCAN Clustering

Clustering is a technique that allows data to be organized into groups of similar objects. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrar...

ver descrição completa

Detalhes bibliográficos
Autor: Fernández Galán, Severino
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universidad Nacional de Educación a Distancia
Repositorio:e-spacio. Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/12467
Acesso em linha:https://hdl.handle.net/20.500.14468/12467
Access Level:acceso abierto
Palavra-chave:Clustering
DBSCAN algorithm
region query strategy
comparative evaluation
id ES_da70e89343adf2233fb6690a5cbc75cd
oai_identifier_str oai:e-spacio.uned.es:20.500.14468/12467
network_acronym_str ES
network_name_str España
repository_id_str
spelling Comparative Evaluation of Region Query Strategies for DBSCAN ClusteringFernández Galán, SeverinoClusteringDBSCAN algorithmregion query strategycomparative evaluationClustering is a technique that allows data to be organized into groups of similar objects. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrary shape. The computational complexity of DBSCAN is dominated by the calculation of the ϵ-neighborhood for every object in the dataset. Thus, the efficiency of DBSCAN can be improved in two different ways: (1) by reducing the overall number of ϵ-neighborhood queries (also known as region queries), or (2) by reducing the complexity of the nearest neighbor search conducted for each region query. This paper deals with the first issue by considering the most relevant region query strategies for DBSCAN, all of them characterized by inspecting the neighborhoods of only a subset of the objects in the dataset. We comparatively evaluate these region query strategies (or DBSCAN variants) in terms of clustering effectiveness and efficiency; additionally, a novel region query strategy is introduced in this work. The results show that some specific DBSCAN variants are only slightly inferior to DBSCAN in terms of effectiveness, while greatly improving its efficiency. Among these variants, the novel one outperforms the rest.Elseviere-Spacio UNED20242024-05-2020192019-10-0120192019-10-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/12467reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0oai:e-spacio.uned.es:20.500.14468/124672026-06-06T12:38:31Z
dc.title.none.fl_str_mv Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
title Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
spellingShingle Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
Fernández Galán, Severino
Clustering
DBSCAN algorithm
region query strategy
comparative evaluation
title_short Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
title_full Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
title_fullStr Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
title_full_unstemmed Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
title_sort Comparative Evaluation of Region Query Strategies for DBSCAN Clustering
dc.creator.none.fl_str_mv Fernández Galán, Severino
author Fernández Galán, Severino
author_facet Fernández Galán, Severino
author_role author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv Clustering
DBSCAN algorithm
region query strategy
comparative evaluation
topic Clustering
DBSCAN algorithm
region query strategy
comparative evaluation
description Clustering is a technique that allows data to be organized into groups of similar objects. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrary shape. The computational complexity of DBSCAN is dominated by the calculation of the ϵ-neighborhood for every object in the dataset. Thus, the efficiency of DBSCAN can be improved in two different ways: (1) by reducing the overall number of ϵ-neighborhood queries (also known as region queries), or (2) by reducing the complexity of the nearest neighbor search conducted for each region query. This paper deals with the first issue by considering the most relevant region query strategies for DBSCAN, all of them characterized by inspecting the neighborhoods of only a subset of the objects in the dataset. We comparatively evaluate these region query strategies (or DBSCAN variants) in terms of clustering effectiveness and efficiency; additionally, a novel region query strategy is introduced in this work. The results show that some specific DBSCAN variants are only slightly inferior to DBSCAN in terms of effectiveness, while greatly improving its efficiency. Among these variants, the novel one outperforms the rest.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-10-01
2019
2019-10-01
2024
2024-05-20
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/12467
url https://hdl.handle.net/20.500.14468/12467
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:e-spacio. Repositorio Institucional de la UNED
instname:Universidad Nacional de Educación a Distancia
instname_str Universidad Nacional de Educación a Distancia
reponame_str e-spacio. Repositorio Institucional de la UNED
collection e-spacio. Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869421578302783488
score 15,812429

Registos relacionados