Title

Autonomous diagnostics: a data mining approach

Document Type

Contribution to Book

Peer Reviewed

1

Publication Date

1-1-2000

Journal/Book/Conference Title

XI Workshop on Supervising and Diagnostics of Machining Systems, Mar 12 - Mar 17 2000

Abstract

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.

Keywords

Sustainability, 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.



This document is currently not available here.

Share

COinS
 

URL

http://ir.uiowa.edu/cee_pubs/376