Paper Info
Title
Towards automated extraction of expert system rules from sales data for the semiconductor market
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.
Year
DOI
Venue
2012
10.1007/978-3-642-37798-3_37
MICAI (2)
Keywords
Field
DocType
expert system rule,expert system,original equipment manufacturers,prediction score,qualitative data mining approach,sales data,probabilistic rule,purchasing behavior,towards automated extraction,semiconductor market,one-year prediction window,chip purchasing policy,quarterly data,data mining,expert systems
Qualitative property,Laptop,Computer science,Expert system,Original equipment manufacturer,Artificial intelligence,Purchasing,Probabilistic logic,Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
5
Name
Order
Citations
PageRank
Jesús Emeterio Navarro-Barrientos101.69
Dieter Armbruster211520.96
Hongmin Li300.34
Morgan Dempsey400.68
Karl G. Kempf515939.76