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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15891
Title: A GH-SOM optimization with SOM labelling and dunn index
Authors: Bokan Garay, Alessandro
Ponce Contreras, Guillermo
Patiño Escarcina, Raquel
Keywords: Cluster validation;Clustering methods;Data sets;Growing hierarchical self-organizing maps;Unsupervised classification;Intelligent systems;Optimization
Issue Date: 2011
Publisher: Scopus
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856748692&doi=10.1109%2fHIS.2011.6122168&partnerID=40&md5=84d450a8bb887d678d92546afd361157
Abstract: Clustering is an unsupervised classification method that divides a data set in groups, where the elements of a group have similar characteristics to each other. A well-known clustering method is the Growing Hierarchical Self-Organizing Map (GH-SOM), that improves the results of an ordinary SOM by controlling the number of neurons generated. In this paper it is proposed a optimization of the typical GH-SOM, using a cluster validation index to verify the quality of partitioning. © 2011 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15891
ISBN: urn:isbn:9781457721502
Appears in Collections:Artículos - Ciencia de la computación

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