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Proceedings Paper

From data to models: synergies of a joint data mining and similarity theory approach
Author(s): Peter Hertkorn; Stephan Rudolph
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Paper Abstract

Successful model building techniques in the natural sciences can usually be classified into the two categories of inductive and deductive methods. If numerical data is the only source of available knowledge in a certain problem, then it is obvious that inductive techniques seem to be the method of choice. Recently, data mining has received much attention to extract hidden knowledge from very large databases, and is, therefore, in this framework considered to be an inductive approach. Deductive methods, on the contrary, rely on a rigorous application and the explicit knowledge of the underlying domain, using first principles to establish a mathematical model of the problem, typically yielding governing equations. This paper focuses on the establishment of an intermediate knowledge representation level, between inductive and deductive knowledge identification techniques. This intermediate knowledge representation level is based on the notion of group transforms, and can be shown to be a mathematically necessary condition in the existence of any correct model representation. The synergy effect of using group transforms in both the inductive data mining and the deductive similarity theory approach can be demonstrated to be conceptually advantageous and is observed to be numerically superior to conventional techniques. The limitations and underlying assumptions of the approach are identified and discussed using example.

Paper Details

Date Published: 25 February 1999
PDF: 8 pages
Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, (25 February 1999); doi: 10.1117/12.339974
Show Author Affiliations
Peter Hertkorn, Univ. of Stuttgart (Germany)
Stephan Rudolph, Univ. of Stuttgart (Germany)

Published in SPIE Proceedings Vol. 3695:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology
Belur V. Dasarathy, Editor(s)

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