Center selection techniques for metric indexes

The metric spaces model formalizes the similarity search concept in nontraditional databases. The goal is to build an index designed to save distance computations when answering similarity queries later. A large class of algorithms to build the index are based on partitioning the space in zones as c...

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Detalles Bibliográficos
Autores principales:	Mendoza Alric, Cristian, Herrera, Norma Edith
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2007
Materias:	Ciencias Informáticas Indexing methods Base de Datos centers selection metric spaces similarity search
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/9535 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-16.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-9535
record_format	dspace
institution	Universidad Nacional de La Plata
institution_str	I-19
repository_str	R-120
collection	SEDICI (UNLP)
language	Inglés
topic	Ciencias Informáticas Indexing methods Base de Datos centers selection metric spaces similarity search
spellingShingle	Ciencias Informáticas Indexing methods Base de Datos centers selection metric spaces similarity search Mendoza Alric, Cristian Herrera, Norma Edith Center selection techniques for metric indexes
topic_facet	Ciencias Informáticas Indexing methods Base de Datos centers selection metric spaces similarity search
description	The metric spaces model formalizes the similarity search concept in nontraditional databases. The goal is to build an index designed to save distance computations when answering similarity queries later. A large class of algorithms to build the index are based on partitioning the space in zones as compact as possible. Each zone stores a representative point, called center, and a few extra data that allow to discard the entire zone at query time without measuring the actual distance between the elements of the zone and the query object. The way in which the centers are selected affects the performance of the algorithm. In this paper, we introduce two new center selection techniques for compact partition based indexes. These techniques were evaluated using the Geometric Near-neighbor Access Tree (GNAT). We experimentally showed that they achieve good performance.
format	Articulo Articulo
author	Mendoza Alric, Cristian Herrera, Norma Edith
author_facet	Mendoza Alric, Cristian Herrera, Norma Edith
author_sort	Mendoza Alric, Cristian
title	Center selection techniques for metric indexes
title_short	Center selection techniques for metric indexes
title_full	Center selection techniques for metric indexes
title_fullStr	Center selection techniques for metric indexes
title_full_unstemmed	Center selection techniques for metric indexes
title_sort	center selection techniques for metric indexes
publishDate	2007
url	http://sedici.unlp.edu.ar/handle/10915/9535 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-16.pdf
work_keys_str_mv	AT mendozaalriccristian centerselectiontechniquesformetricindexes AT herreranormaedith centerselectiontechniquesformetricindexes
bdutipo_str	Repositorios
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Center selection techniques for metric indexes

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