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Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification

Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organi...

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Main Authors: Hasan, Shafaatunnur, Shamsuddin, Siti Mariyam, Yusob, Bariah
Format: Conference or Workshop Item
Published: 2009
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/15152/
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spelling my.utm.151522020-07-20T01:24:09Z http://eprints.utm.my/id/eprint/15152/ Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification Hasan, Shafaatunnur Shamsuddin, Siti Mariyam Yusob, Bariah QA75 Electronic computers. Computer science Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO. 2009 Conference or Workshop Item PeerReviewed Hasan, Shafaatunnur and Shamsuddin, Siti Mariyam and Yusob, Bariah (2009) Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification. In: Konferensi Nasional Teknologi Informasi dan Aplikasinya, 2009, Palembang, Indonesia.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Hasan, Shafaatunnur
Shamsuddin, Siti Mariyam
Yusob, Bariah
Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
description Hybrid technique for Self Organizing Map and Particle Swarm Optimization approach is commonly implemented in clustering area. In this paper, a hybrid approach that is based on Enhanced Self Organizing Map and Particle Swarm Optimization (ESOM/PSO) for classification is proposed. Enhanced Self Organization map which based on Kohonen network structure is to improve the quality of the data classification and labeling. New formulation of hexagonal lattice area is used for the enhancement Self Organizing Map structure. The proposed hybrid ESOM/PSO algorithm uses PSO to evolve the weights for ESOM. The weights are trained by ESOM in the first stage. In the second stage, they are optimized by PSO.
format Conference or Workshop Item
author Hasan, Shafaatunnur
Shamsuddin, Siti Mariyam
Yusob, Bariah
author_facet Hasan, Shafaatunnur
Shamsuddin, Siti Mariyam
Yusob, Bariah
author_sort Hasan, Shafaatunnur
title Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
title_short Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
title_full Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
title_fullStr Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
title_full_unstemmed Enhanced self organizing map (ESOM) and particle swarm optimization (PSO) for classification
title_sort enhanced self organizing map (esom) and particle swarm optimization (pso) for classification
publishDate 2009
url http://eprints.utm.my/id/eprint/15152/
_version_ 1674066113795719168
score 13.211869

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