Abstract
Some of the fundamental and theoretical issues in Knowledge Discovery in Database (KDD) rely on knowledge representation and the use of prior and domain knowledge to extract useful information from data. In many data exploration algorithms, dissimilarity functions do not use domain knowledge for the cases comparison. The Iterative Knowledge Base System (IKBS) has been designed to improve generalization accuracy of exploration algorithms through the use of structural properties of domain models. A general mathematical framework for utilizing structural properties of the domain model encompassing the definition of a Dissimilarity Function for Structured Descriptions is proposed. Applications are conducted with the help of IKBS on a set of databases from the UCI machine learning repository and on structured domain definition data.
Chapter PDF
Similar content being viewed by others
References
Diatta J., Grosser D., and Ralambondrainy H. A general dissimilarity measure for complex data. INF 01, IREMIA, University of Reunion Island, july 1999.
Merz and Murphy. Uci repository of machine learning databases. Department of Information and Computer Science, 1996.
Conruyt N. and Grosser D. Managing complex knowledge in natural sciences. LNCS 1650, Springer Verlag, pages 401–414, 1999.
Schaffer and Cullen. A conservation law for generalization performance. In Proceedings of ML’94, 1994.
Cover T. and Hart P.Nearest neighbor pattern classiffcation. Institute of Electrical and Electronics Enginneers Transactions on Information Theory, Vol.13, No.1,pages 21–27, 1967.
Fayyad U.M., Piatetsky-Shapiro G., Padhraic Smyth, and Ramasamy Uthurusamy, editors. From Data Mining to Knowledge Discovery: Current Challenges and Future Directions. Advances in Knowledge Discovery and Data Mining, AAAI Press / MIT Press, 1996.
Klösgen W. and Zytkow J.M. Knowledge Discovery in Databases Terminology. Advances in Knowledge Discovery and Data Mining, AAAI Press, 1996.
Randall W.D. and Martinez T.R. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research, 6, pages 1–34, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grosser, D., Diatta, J., Conruyt, N. (2000). Improving Dissimilarity Functions with Domain Knowledge, applications with IKBS system. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_44
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_44
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41066-9
Online ISBN: 978-3-540-45372-7
eBook Packages: Springer Book Archive