Autonomous diagnostics: a data mining approach
Contribution to Book
XI Workshop on Supervising and Diagnostics of Machining Systems, Mar 12 - Mar 17 2000
Data mining offers tools for data analysis, knowledge discovery, and autonomous decision-making. In this paper, a data mining approach is used to extract meaningful features (attributes) from a data set and make accurate predictions for a semiconductor process application. An important property of the approach discussed in this paper is that a decision is made only when it is accurately predicted, otherwise no autonomous decision is recommended. The high accuracy of predictions made by the proposed approach is based on a weak assumption that objects with equivalent values of a subset of attributes produce equivalent outcomes.
Published Article/Book Citation
In: XI Workshop on Supervising and Diagnostics of Machining Systems, Mar 12 - Mar 17 2000. Karpacz, Poland: Wydawnictwo Politechniki Wroclawskiej, 2000.
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