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

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Classifi cation, Patterns, Self-organising map, SOM, Multilevel learning, Distance (or dissimilarity) measure, Predictions, Computational times, Classification rate.
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	UNSPECIFIED
Depositing User:	Mrs. Norazmilah Yaakub
Date Deposited:	19 Jul 2010 07:42
Last Modified:	19 Jul 2010 07:42
URI:	https://repo.uum.edu.my/id/eprint/295

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