Document Type

Article

Department or Administrative Unit

Computer Science

Publication Date

4-16-2008

Abstract

Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a subject domain, a many-sorted empirical system (algebraic structure in the first-order logic), and an ontology of a KDD&DM method. The paper concludes with a review of our RDM approach and 'Discovery' system built on this methodology that can analyze any hypotheses represented in the first-order logic and use any input by representing it in many-sorted empirical system.

Comments

Please note: This is the author’s version of a work. Changes may have been made to this work since it was submitted for publication. The final publication is available at IOS Press here.

Journal

Intelligent Data Analysis

Rights

© 2008 – IOS Press and the authors. All rights reserved

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