Title :
Decomposition in data mining: an industrial case study
Author_Institution :
Intelligent Syst. Lab., Iowa Univ., Iowa City, IA, USA
fDate :
10/1/2000 12:00:00 AM
Abstract :
Data mining offers tools for discovery of relationships, patterns, and knowledge in large databases. The knowledge extraction process is computationally complex and therefore a subset of all data Is normally considered for mining. In this paper, numerous methods for decomposition of data sets are discussed. Decomposition enhances the quality of knowledge extracted from large databases by simplification of the data mining task. The ideas presented are illustrated with examples and an industrial case study. In the case study reported in this paper, a data mining approach is applied to extract knowledge from a data set. The extracted knowledge is used for the prediction and prevention of manufacturing faults in wafers
Keywords :
data mining; decision support systems; integrated circuit manufacture; manufacturing data processing; quality control; very large databases; data mining; data set; decomposition; industrial case study; knowledge extraction process; large databases; manufacturing faults; wafers; Computer aided software engineering; Data mining; Databases; Decision making; Decision trees; Machine learning; Machine learning algorithms; Manufacturing; Mathematical model; Mining industry;
Journal_Title :
Electronics Packaging Manufacturing, IEEE Transactions on
DOI :
10.1109/6104.895081