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Proceedings Paper

Attributes significance elucidation as a knowledge discovery tool under case-based reasoning
Author(s): Belur V. Dasarathy
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Paper Abstract

Case based reasoning (CBR) has come to be recognized as one of the standard approaches in the context of data mining and knowledge discovery to determine the best match of a new case among the currently defined cases in the database. It is well known that CBR is highly sensitive to the attribute space in which such reasoning is carried out. A priori assessment of the attributes to determine the relevancy and effectiveness of the potential attributes is often carried out using feature selection techniques adapted from the pattern recognition world. It would be beneficial to be able to elucidate the significance of the attributes so selected on the decisions made by the CBR process on the individual cases. Towards this end, the concept of elucidation proposed recently in the context of information fusion systems is adapted here to develop a methodology for attribute significance assessment. The methodology is illustrated using a couple of well-known data sets available in the literature and/or on the Internet.

Paper Details

Date Published: 12 April 2004
PDF: 12 pages
Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); doi: 10.1117/12.543583
Show Author Affiliations
Belur V. Dasarathy, Consultant (United States)


Published in SPIE Proceedings Vol. 5433:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI
Belur V. Dasarathy, Editor(s)

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