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Rule Extraction from Experimental Data for Manufacturing Process Design

  • Kit Yan Chan
  • C. K. Kwong
  • Tharam S. Dillon
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 403)

Introduction

In order to study a manufacturing process, it is common for engineers to conduct a large number of experiments and generate experimental data sets. Experimental data sets must first be processed and/or analyzed in order to extract patterns, useful information or knowledge. The development of effective and efficient methods for deriving knowledge from these data is important as the knowledge extracted from the data not only has to have a high predictive accuracy, but also needs to be understood by users [Fayyad et al. 1996, Freitas 1997, Freitas 1999]. Rule induction is one of the common forms of data mining [Langlery and Simon 1995]. It is a method for discovering a set of “IF THEN” rules that can be used for converting uninformative data into either a knowledge base for decision support or an easily understood description of the system behavior so that knowledge that humans can understand can be explored. Moreover, it is able to search for all possible interesting patterns from data sets.

Keywords

Genetic Algorithm Computational System Discovery System Inductive Logic Programming Rule Induction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Kit Yan Chan
    • 1
  • C. K. Kwong
    • 2
  • Tharam S. Dillon
    • 1
  1. 1.Digital Ecosystems and BusinessCurtin University of TechnologyPerthAustralia
  2. 2.Department of Industrial and SystemsThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR

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