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.
Visually Mining Off-line Data for Quality Improvement / Ragozini, G., Porzio, G.C.. - In: QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL. - ISSN 0748-8017. - 19:4(2003), pp. 273-283. [10.1002/qre.588]
Visually Mining Off-line Data for Quality Improvement
RAGOZINI, GIANCARLO;
2003
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
