Characterization and analysis of sales data for the semiconductor market: An expert system approach
Chip purchasing policies of the Original Equipment Manufacturers (OEMs) of laptop computers are characterized by similarity measures and probabilistic rules. Our main goal is to build an expert system for predicting purchasing behavior in the semiconductor market. The probabilistic rules and similarity measures are extracted from data of products bought by the OEMs in the semiconductor market over twenty quarters. We present the data collected and different qualitative data mining approaches to analyze and extract rules from the data that best characterize the purchasing behavior of the OEMs. Our analysis of the similar product selection shows that there are two main groups of OEMs buying similar products. Using our probabilistic rules, we obtain an average score of approximately 95% reconstructing quarterly data for a one year window.
semiconductor market expert systems similarity measures probabilistic rules data mining