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12 April 2004 Attributes significance elucidation as a knowledge discovery tool under case-based reasoning
Belur V. Dasarathy
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Proceedings Volume 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI; (2004) https://doi.org/10.1117/12.543583
Event: Defense and Security, 2004, Orlando, Florida, United States
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.
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Belur V. Dasarathy "Attributes significance elucidation as a knowledge discovery tool under case-based reasoning", Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); https://doi.org/10.1117/12.543583
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KEYWORDS
Knowledge discovery
Data mining
Databases
Feature selection
Information fusion
Internet
Pattern recognition
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