Title :
Using data mining methods for improvement of product and process quality
Author :
Estler, Manfred ; Hilpert, Ralf ; Kiupel, Niels ; Soravia, Sergio
Author_Institution :
Konzembereich Verfahrens- und Prozesstech., Degussa-Huls AG, Hanau, Germany
fDate :
Aug. 31 1999-Sept. 3 1999
Abstract :
Recently, data analysis methods have been successfully employed in various areas of industry. With their help analysts gain valuable information and knowledge about business and technical processes. In addition to established and proven statistical methods, new data analysis techniques hiding behind catchwords such as Data Mining, Knowledge Discovery in Databases, and Computational Intelligence are increasingly used. These new approaches are primarily characterized by their ability to find conspicuous patterns in databases almost on their own. In a particular way, they support the general goal to detect existing and possibly still hidden information in databases. In chemical industry the information obtained is mainly meant to support the design of products and production processes, as well as the development of intelligent process management strategies.
Keywords :
chemical engineering computing; data analysis; data mining; product quality; production engineering computing; statistical analysis; chemical industry; computational intelligence; data analysis methods; data mining methods; intelligent process management strategy; knowledge discovery in databases; process quality; product quality; statistical methods; Data analysis; Data mining; Databases; Product design; Quality assessment; Safety; Statistical analysis; Exploratory data analysis; data mining; data preprocessing; independence analysis; statistical methods;
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5