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dc.date.accessioned 2008-05-22T16:52:09Z
dc.date.available 2008-05-22T03:00:00Z
dc.date.issued 2007-04
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/9535
dc.description.abstract 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. en
dc.format.extent 98-104 es
dc.language en es
dc.subject Indexing methods es
dc.subject Base de Datos es
dc.subject metric spaces en
dc.subject similarity search en
dc.subject centers selection es
dc.title Center selection techniques for metric indexes en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Mar07-16.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Mendoza Alric, Cristian es
sedici.creator.person Herrera, Norma Edith es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000585 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 7, no. 1 es


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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)