自己組織化マップを用いた化学分析での情報抽出 [in Japanese] Data Mining of Chemical Analysis [in Japanese]
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The Self-Organizing Map (SOM) developed by Teuvo Kohonen is a powerful tool for Data Mining or knowledge discovery and visualization of high dimensional data. The SOM is being applied to problems of chemical analysis. It simultaneously performs topology preservation of the data space whiles quantizing the data space formed by the input data. The compositions of the unlabeled spectra whose compositions are unknown can be determined using the SOM method which uses the labeled spectra whose compositions are known. In this study, the data mining capabilities of SOM are examined using data from Auger Electron Spectroscopy (AES) and X-ray Photoelectric Spectroscopy (XPS). The results obtained are compared to determine which data is more adaptive to the SOM.
- Journal of the Vacuum Society of Japan
Journal of the Vacuum Society of Japan 43(3), 263-267, 2000-03-20
The Vacuum Society of Japan