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Application to Bankruptcy Prediction in Banks
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Author(s): Joaquín Ordieres-Meré (Universidad Politécnica de Madrid, Spain), Ana González-Marcos (Universidad de La Rioja, Spain), Manuel Castejón-Limas (Universidad de León, Spain)and Francisco J. Martínez-de-Pisón (Universidad de La Rioja, Spain)
Copyright: 2010
Pages: 13
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch020
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Abstract
This chapter reports five experiences in successfully applying different data mining techniques in a hotdip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models either for their processes or products using classical techniques. Their need to continuously cut costs down while increasing productivity and product quality is now pushing the industry into using data mining techniques so as to gain deeper insights into their manufacturing processes. The authors’ work was aimed at extracting hidden knowledge from massive data bases in order to improve the existing control systems. The results obtained, though small at first glance, lead to huge savings at such high volume production environment. The effective solutions provided by the use of data mining techniques along these projects encourages the authors to continue applying this data driven approach to frequent hard-to-solve problems in the steel industry.
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