Preserving the topology of self-organizing maps for data analysis: A review
In Kohonen's Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
IOP Publishing
2020
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/29756/1/36.%20Preserving%20the%20Topology%20of%20Self-Organizing%20Maps%20for%20Data%20Analysis-%20A%20Review.pdf https://umpir.ump.edu.my/id/eprint/29756/ https://doi.org/10.1088/1757-899X/769/1/012004 |
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| Summary: | In Kohonen's Self-Organizing Maps (SOM) algorithm, preserving the map structure to represent the real input patterns appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. This paper presents detail explanation on SOM learning algorithm and its applications. Some issues related to SOM's architecture are also discussed, namely the formulation of training data from input samples, and the Best Matching Unit (BMU) identification for better visualization of large datasets, and improvement made to the SOM algorithm. |
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