Abstract
The solution obtained by Self-Organizing Map (SOM) strongly depends on the initial cluster centers. However, all existing SOM initialization methods do not guarantee to obtain a better minimal solution. Generally, we can group these methods in two classes: random initialization and data analysis based initialization classes. This work proposes an improvement of linear projection initialization method. This method belongs to the second initialization class. Instead of using regular rectangular grid our method combines a linear projection technique with irregular rectangular grid. By this way the distribution of results produced by the linear projection technique is considred. The experiments confirm that the proposed method gives better solutions compared to its original version.
Keywords
- Rectangular Grid
- Linear Projection
- Random Initialization
- Initialization Method
- Irregular Grid
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Attik, M., Bougrain, L., Alexandre, F. (2005). Self-organizing Map Initialization. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_56
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DOI: https://doi.org/10.1007/11550822_56
Publisher Name: Springer, Berlin, Heidelberg
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