Title

Decomposition in data mining: An industrial case study

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2000

Journal/Book/Conference Title

IEEE Transactions on Electronics Packaging Manufacturing

Volume

23

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

Sustainability, 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.

Published Article/Book Citation

IEEE Transactions on Electronics Packaging Manufacturing, 23:4 (2000) pp.345-353.



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URL

http://ir.uiowa.edu/cee_pubs/374