Towards automated extraction of expert system rules from sales data for the semiconductor market

Authors:
Jesús Emeterio Navarro-Barrientos;Dieter Armbruster;Hongmin Li;Morgan Dempsey;Karl G. Kempf
Affiliations:
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ;School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ;W.P. Carey School of Business, Arizona State University, Tempe, AZ;Intel Corporation, Chandler, AZ;Intel Corporation, Chandler, AZ
Venue:
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Year:
2012

Citing 3
Cited 0

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Managing Short Life-Cycle Technology Products for Agere Systems

Interfaces
Improving New-Product Forecasting at Intel Corporation

Interfaces

Quantified Score

Hi-index	0.00

Visualization

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.