A GH-SOM optimization with SOM labelling and dunn index

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Date
2011
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Publisher
Scopus
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.
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Keywords
Cluster validation , Clustering methods , Data sets , Growing hierarchical self-organizing maps , Unsupervised classification , Intelligent systems , Optimization
Citation
URI
https://hdl.handle.net/20.500.12590/15891
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Artículos - Ciencias de la Computación