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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15805
Title: OVMMSOM: A Variation of MMSOM and VMSOM as a Clusterization Technique
Authors: Sánchez Huertas, Franco
Patiño Escarcina, Raquel
Túpac Valdivia, Yván Jesús
Keywords: Conformal mapping;Neural networks;CDbw index;Clustering;Clusterization;Order statistics;Participation index;Training model;Validity index;Within clusters;Self organizing maps
Issue Date: 2017
Publisher: IEEE Computer Society
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85011928415&doi=10.1109%2fSCCC.2013.31&partnerID=40&md5=8603b5026fcbc018ebe533edab033372
Abstract: In this paper the Optimized Vector and Marginal Median Self-Organizing Map (OVMMSOM) was proposed as a new method of train Self-Organizing Maps (SOM). This variant is based on order statistics, Marginal Median SOM (MMSOM) and Vector Median SOM (VMSOM). This training model combines MMSOM and VMSOM defining their particular importance through a ? participation index. To demonstrate the effectiveness of the proposal, images from the COIL100 data set was clusterized and the Compose Density between and within clusters (CDbw) validity index was used. The performed experiments show that the proposed model outperforms standard SOM network trained in batch and even results from MMSOM and VMSOM by separately. © 2015 IEEE.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15805
ISBN: 9781509004263
ISSN: 15224902
Appears in Collections:Artículos de investigación

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