Abstract
This paper describes the integration of ILP with Clementine. Background on ILP and Clementine is provided, with a description of Clementine’s target users. The benefits of ILP to data mining are outlined, and ILP is compared with pre-existing data mining algorithms. Issues of integration between ILP and Clementine are discussed. The implementation is then described, showing how the key issues are addressed, and describing in brief the Clementine mechanisms used to integrate ILP.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
8 References
H. Blockeel & L. De Raedt, Tilde and Warmr User Manual, Version 2.0, Katholieke Universiteit Leuven, April 1999.
S. Brewer & T. Khabaza. Guidelines for ILP in Data Mining, Version 1.5, ALADIN Project Internal Report, November 1999.
L. Dehaspe, WARMR The Frequent Query Discovery Engine User’s Guide 2.1, October 1998.
T. Khabaza, Note on the Integration of Inductive Logic Programming with the Clementine Data Mining System, ALADIN Project Internal Report, June 1998.
S. Muggleton & J. Firth, CProgol4.4: Theory and Use, University of York, June 1998.
Integral Solutions Ltd, “Introduction to the External Module Interface”, Clementine Reference Manual, version 5, September 1998.
S. Muggleton, “Inverse Entailment and Progol”, New Generation Computing, 13:245–286, 1995.
H. Blockeel and L. DeRaedt, “Top-down Induction of first order Logical Decision Trees”, Artificial Intelligence 101 (1–2), 1998.
L. Dehaspe and H. Toivonen, Frequent query discovery: a unifying ILP approach to association rule mining. Technical Report CW-258, Department of Computer Science, Katholieke Universiteit Leuven, March 1998. http://www.cs.kuleuven.ac.be/publicaties/-rapporten/CW1998.html
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
Brewer, S., Khabaza, T. (2000). Inductive Logic Programming in Clementine. 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_34
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_34
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