Multilevel kohonen network learning for clustering problems
Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in th...
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Universiti Utara Malaysia
2008
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my.uum.repo.2952010-07-19T07:42:01Z http://repo.uum.edu.my/295/ Multilevel kohonen network learning for clustering problems Shamsuddin, Siti Mariyam Zainal, Anazida Mohd Yusof, Norfadzila TK Electrical engineering. Electronics Nuclear engineering Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM were evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset. Universiti Utara Malaysia 2008 Article PeerReviewed application/pdf en http://repo.uum.edu.my/295/1/Siti_Mariyam_Shamsuddin.pdf Shamsuddin, Siti Mariyam and Zainal, Anazida and Mohd Yusof, Norfadzila (2008) Multilevel kohonen network learning for clustering problems. Journal of ICT, 7. pp. 1-25. ISSN 1675-414X http://jict.uum.edu.my |
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TK Electrical engineering. Electronics Nuclear engineering Shamsuddin, Siti Mariyam Zainal, Anazida Mohd Yusof, Norfadzila Multilevel kohonen network learning for clustering problems |
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Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant class. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task
of pattern separation. The performance of standard SOM and
multilevel SOM were evaluated with different distance or
dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset. |
format |
Article |
author |
Shamsuddin, Siti Mariyam Zainal, Anazida Mohd Yusof, Norfadzila |
author_facet |
Shamsuddin, Siti Mariyam Zainal, Anazida Mohd Yusof, Norfadzila |
author_sort |
Shamsuddin, Siti Mariyam |
title |
Multilevel kohonen network learning for clustering problems |
title_short |
Multilevel kohonen network learning for clustering problems |
title_full |
Multilevel kohonen network learning for clustering problems |
title_fullStr |
Multilevel kohonen network learning for clustering problems |
title_full_unstemmed |
Multilevel kohonen network learning for clustering problems |
title_sort |
multilevel kohonen network learning for clustering problems |
publisher |
Universiti Utara Malaysia |
publishDate |
2008 |
url |
http://repo.uum.edu.my/295/1/Siti_Mariyam_Shamsuddin.pdf http://repo.uum.edu.my/295/ http://jict.uum.edu.my |
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1644277758959812608 |
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