Abstract
Chip purchasing policies of the Original Equipment Manufacturers (OEMs) of laptop computers are characterized by probabilistic rules. The rules are extracted from data on products bought by the OEMs in the semiconductor market over twenty quarters. We present the data collected and a qualitative data mining approach to extract probabilistic rules from the data that best characterize the purchasing behavior of the OEMs. We validate and simulate the extracted probabilistic rules as a first step towards building an expert system for predicting purchasing behavior in the semiconductor market. Our results show a prediction score of approximately 95% over a one-year prediction window for quarterly data.
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Navarro-Barrientos, J.E., Armbruster, D., Li, H., Dempsey, M., Kempf, K.G. (2013). Towards Automated Extraction of Expert System Rules from Sales Data for the Semiconductor Market. In: Batyrshin, I., Mendoza, M.G. (eds) Advances in Computational Intelligence. MICAI 2012. Lecture Notes in Computer Science(), vol 7630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37798-3_37
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DOI: https://doi.org/10.1007/978-3-642-37798-3_37
Publisher Name: Springer, Berlin, Heidelberg
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