DocumentCode :
2918720
Title :
A GH-SOM optimization with SOM labelling and dunn index
Author :
Garay, Alessandro Bokan ; Contreras, Guillermo Ponce ; Escarcina, Raquel Patiño
Author_Institution :
Sch. of Comput. Sci., San Pablo Catholic Univ., Arequipa, Peru
fYear :
2011
fDate :
5-8 Dec. 2011
Firstpage :
572
Lastpage :
577
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.
Keywords :
optimisation; self-organising feature maps; Dunn index; SOM labelling; cluster validation index; clustering method; growing hierarchical self-organizing map; neurons; unsupervised classification method; Dispersion; Indexes; Labeling; Neurons; Proposals; Training; Cluster Validation Index; Growing Hierarchical Self-Organized Map;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2011 11th International Conference on
Conference_Location :
Melacca
Print_ISBN :
978-1-4577-2151-9
Type :
conf
DOI :
10.1109/HIS.2011.6122168
Filename :
6122168
Link To Document :
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