Shamsuddin, Siti Mariyam and Zainal, Anazida and Mohd Yusof, Norfadzila
Multilevel kohonen network learning for clustering problems.
Journal of ICT, 7.
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.
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