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Proceedings Paper

Data mining and decision making
Author(s): Andrew Kusiak
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Paper Abstract

Models and algorithms for effective decision-making in a data-driven environment are discussed. To enhance the quality of the extracted knowledge and decision-making, the data sets are transformed, the knowledge is extracted with multiple algorithms, the impact of the decisions on the modeled process is simulated, and the parameters optimizing process performance are recommended. The applications discussed in this paper differ from most data mining tasks, where the extracted knowledge is used to assign decision values to new objects that have not been included in the training data. For example, in a typical data mining application the equipment fault is recognized based on the failure symptoms. In this paper, a subset of rules is selected from the extracted knowledge to meet the established decision-making criteria. The parameter values represented by the conditions of this set of rules are called a decision signature. A model and algorithms for the selection of the desired parameters (decision signatures) will be developed. The parameters of this model are updated using a framework provided by the learning classifier systems.

Paper Details

Date Published: 12 March 2002
PDF: 11 pages
Proc. SPIE 4730, Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, (12 March 2002); doi: 10.1117/12.460224
Show Author Affiliations
Andrew Kusiak, Univ. of Iowa (United States)

Published in SPIE Proceedings Vol. 4730:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV
Belur V. Dasarathy, Editor(s)

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