Método heurístico para particionamiento óptimo

 

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Detalles Bibliográficos
Autores: de-los-Cobos-Silva, Sergio G., Trejos Zelaya, Javier, Pérez Salvador, Blanca Rosa, Gutiérrez Andrade, Miguel Ángel
Formato: artículo original
Estado:Versión publicada
Fecha de Publicación:2003
Descripción:Many data analysis problems deal with non supervised partitioning of a data set, in non empty clusters well separated between them and homogeneous within the clusters. An ideal partitioning is obtained when any object can be assigned a class without ambiguity. The present paper has two main parts; first, we present different methods and heuristics that find the number of clusters for optimal partitioning of a set; afterwards, we propose a new heuristic and we perform different comparisons in order to evaluate the advantages on well known data sets; we end the paper with some concluding remarks.
País:Portal de Revistas UCR
Institución:Universidad de Costa Rica
Repositorio:Portal de Revistas UCR
Lenguaje:Español
OAI Identifier:oai:portal.ucr.ac.cr:article/221
Acceso en línea:https://revistas.ucr.ac.cr/index.php/matematica/article/view/221
Access Level:acceso abierto
Palabra clave:Optimal partitioning
clustering
classification
heuristics
Particionamiento óptimo
clasificación
heurísticas