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.
Journal article
Decomposition in data mining: An industrial case study
IEEE Transactions on Electronics Packaging Manufacturing, Vol.23(4), pp.345-353
2000
DOI: 10.1109/6104.895081
Abstract
Details
- Title: Subtitle
- Decomposition in data mining: An industrial case study
- Creators
- Andrew Kusiak - University of Iowa
- Resource Type
- Journal article
- Publication Details
- IEEE Transactions on Electronics Packaging Manufacturing, Vol.23(4), pp.345-353
- DOI
- 10.1109/6104.895081
- ISSN
- 1521-334X
- Language
- English
- Date published
- 2000
- Academic Unit
- Industrial and Systems Engineering; Nursing
- Record Identifier
- 9983557506602771
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