Decomposition in data mining: an industrial case study

Author

Kusiak, Andrew

Author_Institution

Intelligent Syst. Lab., Iowa Univ., Iowa City, IA, USA

Volume

23

Issue

4

fYear

2000

fDate

10/1/2000 12:00:00 AM

Firstpage

345

Lastpage

353

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;

fLanguage

English

Journal_Title

Electronics Packaging Manufacturing, IEEE Transactions on

Publisher

ieee

ISSN

1521-334X

Type

jour

DOI

10.1109/6104.895081

Filename

895081