SOM+PSO : A novel method to obtain classification rules

Currently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, maki...

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Detalles Bibliográficos
Autores principales:	Lanzarini, Laura Cristina, Villa Monte, Augusto, Ronchetti, Franco
Formato:	Articulo
Lenguaje:	Inglés
Publicado:	2015
Materias:	Ciencias Informáticas Data mining clasificación adaptive strategies self-organizing maps particle swarm optimization
Acceso en línea:	http://sedici.unlp.edu.ar/handle/10915/44720 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-3.pdf
Aporte de:	SEDICI (UNLP) de Universidad Nacional de La Plata

id	I19-R120-10915-44720
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 Data mining clasificación adaptive strategies self-organizing maps particle swarm optimization
spellingShingle	Ciencias Informáticas Data mining clasificación adaptive strategies self-organizing maps particle swarm optimization Lanzarini, Laura Cristina Villa Monte, Augusto Ronchetti, Franco SOM+PSO : A novel method to obtain classification rules
topic_facet	Ciencias Informáticas Data mining clasificación adaptive strategies self-organizing maps particle swarm optimization
description	Currently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called SOM+PSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with the PART method and measured over 19 databases (mostly from the UCI repository), and satisfactory results were obtained.
format	Articulo Articulo
author	Lanzarini, Laura Cristina Villa Monte, Augusto Ronchetti, Franco
author_facet	Lanzarini, Laura Cristina Villa Monte, Augusto Ronchetti, Franco
author_sort	Lanzarini, Laura Cristina
title	SOM+PSO : A novel method to obtain classification rules
title_short	SOM+PSO : A novel method to obtain classification rules
title_full	SOM+PSO : A novel method to obtain classification rules
title_fullStr	SOM+PSO : A novel method to obtain classification rules
title_full_unstemmed	SOM+PSO : A novel method to obtain classification rules
title_sort	som+pso : a novel method to obtain classification rules
publishDate	2015
url	http://sedici.unlp.edu.ar/handle/10915/44720 http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Apr15-3.pdf
work_keys_str_mv	AT lanzarinilauracristina sompsoanovelmethodtoobtainclassificationrules AT villamonteaugusto sompsoanovelmethodtoobtainclassificationrules AT ronchettifranco sompsoanovelmethodtoobtainclassificationrules
bdutipo_str	Repositorios
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SOM+PSO : A novel method to obtain classification rules

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