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Visually Mining Off‐line Data for Quality Improvement
Author(s) -
Porzio Giovanni C.,
Ragozini Giancarlo
Publication year - 2003
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.588
Subject(s) - computer science , process (computing) , data mining , quality (philosophy) , production line , set (abstract data type) , product (mathematics) , production (economics) , data science , engineering , mechanical engineering , philosophy , geometry , mathematics , epistemology , economics , macroeconomics , programming language , operating system
Highly automated modern manufacturing processes are yielding large databases with records on hundreds of process variables and product characteristics. This large amount of information calls for new approaches to production process analysis. In this paper, we discuss why a data mining framework can be appropriate for this goal, and we propose a visual data mining strategy to mine large and high‐dimensional off‐line data sets. The strategy allows users to achieve a deeper process understanding through a set of linked interactive graphical devices, and is illustrated within an industrial process case study. Copyright © 2003 John Wiley & Sons, Ltd.