Abstract
In the field of explorative data analysis self-organizing maps have been used successfully for a lot of applications. In our case, we apply the self-organizing map for the analysis of semiconductor fabrication data by training recorded high dimensional data sets. Usually, the training result is displayed by using appropriate visualization techniques and the results are evaluated manually. Especially for large data sets an automated post-processing of the training result is essential. In this paper an automatic training result analysis based on specific image processing is introduced. Dependencies between components maps are calculated by structure overlapping analysis based on the segmentation of component maps. This novel method has been integrated into the data analysis software DanI, that simulates self-organizing maps for data analysis with several pre-processing and post-processing capabilities.
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Franzmeier, M., Witkowski, U., Rückert, U. (2005). Explorative Data Analysis Based on Self-organizing Maps and Automatic Map Analysis. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_89
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DOI: https://doi.org/10.1007/11494669_89
Publisher Name: Springer, Berlin, Heidelberg
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