Knowledge extraction in large databases using adaptive strategies

The general objective of this thesis is the development of an adaptive technique for extracting knowledge in large databases. Nowadays, technology allows storing huge volumes of information. For this reason, the availability of techniques that allow, as a first stage, analyzing that information and...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autor principal: Hasperué, Waldo
Formato: Articulo Revision
Lenguaje:Inglés
Publicado: 2013
Materias:
Ciencias Informáticas
Base de Datos
Almacenamiento y Recuperación de la Información
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/26180
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr13-TO1.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-26180
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
Base de Datos
Almacenamiento y Recuperación de la Información
spellingShingle Ciencias Informáticas
Base de Datos
Almacenamiento y Recuperación de la Información
Hasperué, Waldo
Knowledge extraction in large databases using adaptive strategies
topic_facet Ciencias Informáticas
Base de Datos
Almacenamiento y Recuperación de la Información
description The general objective of this thesis is the development of an adaptive technique for extracting knowledge in large databases. Nowadays, technology allows storing huge volumes of information. For this reason, the availability of techniques that allow, as a first stage, analyzing that information and obtaining knowledge that can be expressed as classification rules, is of interest. However, the information available is expected to change and/or increase with time, and therefore, as a second stage, it would be relevant to adapt the knowledge acquired to the changes or variations affecting the original data set. The contribution of this thesis is focused on the definition of an adaptive technique that allows extracting knowledge from large databases using a dynamic model that can adapt to information changes, thus obtaining a data mining technique that can generate useful knowledge and produce results that the end user can exploit. The results of this research work can be applied to areas such as soil analysis, genetic analysis, biology, robotics, economy, medicine, plant failure detection, and mobile systems communications. In these cases, obtaining an optimal result is important, since this helps improve the quality of the decisions made after the process.
format Articulo
Revision
author Hasperué, Waldo
author_facet Hasperué, Waldo
author_sort Hasperué, Waldo
title Knowledge extraction in large databases using adaptive strategies
title_short Knowledge extraction in large databases using adaptive strategies
title_full Knowledge extraction in large databases using adaptive strategies
title_fullStr Knowledge extraction in large databases using adaptive strategies
title_full_unstemmed Knowledge extraction in large databases using adaptive strategies
title_sort knowledge extraction in large databases using adaptive strategies
publishDate 2013
url http://sedici.unlp.edu.ar/handle/10915/26180
http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr13-TO1.pdf
work_keys_str_mv AT hasperuewaldo knowledgeextractioninlargedatabasesusingadaptivestrategies
bdutipo_str Repositorios
_version_ 1764820467306201089

Ejemplares similares