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http://dx.doi.org/10.5391/IJFIS.2005.5.3.200

Datamining: Roadmap to Extract Inference Rules and Design Data Models from Process Data of Industrial Applications  

Bae Hyeon (School of Electrical and Computer Engineering, Pusan National University)
Kim Youn-Tae (School of Electrical and Computer Engineering, Pusan National University)
Kim Sung-Shin (School of Electrical and Computer Engineering, Pusan National University)
Vachtsevanos George J. (School of Electrical and Computer Engineering, Georgia Institute of Technology)
Publication Information
International Journal of Fuzzy Logic and Intelligent Systems / v.5, no.3, 2005 , pp. 200-205 More about this Journal
Abstract
The objectives of this study were to introduce the easiest and most proper applications of datamining in industrial processes. Applying datamining in manufacturing is very different from applying it in marketing. Misapplication of datamining in manufacturing system results in significant problems. Therefore, it is very important to determine the best procedure and technique in advance. In previous studies, related literature has been introduced, but there has not been much description of datamining applications. Research has not often referred to descriptions of particular examples dealing with application problems in manufacturing. In this study, a datamining roadmap was proposed to support datamining applications for industrial processes. The roadmap was classified into three stages, and each stage was categorized into reasonable classes according to the datamining purposed. Each category includes representative techniques for datamining that have been broadly applied over decades. Those techniques differ according to developers and application purposes; however, in this paper, exemplary methods are described. Based on the datamining roadmap, nonexperts can determine procedures and techniques for datamining in their applications.
Keywords
Knowledge & Rule Extraction; Datamining Roadmap; Industrial Applications;
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  • Reference
1 R. Brachman, T. Khabaza, W. Kloesgen, G. Piatetsky-Shapiro, and E. Simoudis, 'Mining Business Databases,' Communications of the ACM, vol. 39, no. 11, pp. 42-48, 1996   DOI   ScienceOn
2 M. Berry and G. Linoff, Datamining Techniques for Marketing, Sales, and Customer Support, New York: Wiley Computer Publishing, 1997
3 P. Adriaans and D. Zantinge, 'Datamining,' Harlow, England, Reading, Mass.: Addison-Wesley, 1996
4 B. V. Dasarathy (Eds.), 'Datamining and knowledge discovery: theory, tools, and technology II,' Orlando, Florida, Bellingham, Wash.: SPIE, 2000
5 M. A. Bramer (Editor), 'Knowledge discovery and datamining,' London: The Institution of Electrical Engineers, 1999
6 J. Han, 'Datamining: concepts and techniques,' San Francisco: Morgan Kaufmann Publishers, 2001
7 K. J. Cios, 'Datamining methods for knowledge discovery,' Boston: Kluwer Academic, 1998