Abstract
Relational rule learning is typically used in solving classification and prediction tasks. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach, applicable to subgroup discovery in individualcentered domains, was successfully applied to two standard ILP problems (East-West trains and KRK) and a real-life telecommunications application.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetski-Shapiro, P. Smyth and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, 307–328. AAAI Press, 1996.
A. Srinivasan and R. D. King. Feature construction with Inductive Logic Programming: A study of quantitative predictions of biological activity aided by structural attributes. Data Mining and Knowledge Discovery, 3(1):37–57, 1999.
B. Cestnik. Estimating probabilities: A crucial task in machine learning. In L. Aiello, editor, Proc. of the 9th European Conference on Artificial Intelligence, 147–149. Pitman, 1990.
P. Clark and R. Boswell. Rule induction with CN2: Some recent improvements. In Y. Kodratoff, editor, Proc. of the 5th European Working Session on Learning, 151–163. Springer, 1989.
P. Clark and T. Niblett. Induction in noisy domains. In I. Bratko and N. Lavrač, editors, Progress in Machine Learning (Proc. of the 2nd European Working Session on Learning), 11–30. Sigma Press, 1987.
P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3(4):261–283, 1989.
S. Džeroski, B. Cestnik, and I. Petrovski. (1993) Using the m-estimate in rule induction. Journal of Computing and Information Technology, 1(1):37–46, 1993.
C. Ferri-Ramírez, P. A. Flach, and J. Hernandez-Orallo. Learning decision trees using the area under the ROC curve. In Proc. of the 19th International Conference on Machine Learning, 139–146. Morgan Kaufmann, 2002.
P. A. Flach and N. Lachiche. 1BC: A first-order Bayesian classifier. In Proc. of the 9th International Workshop on Inductive Logic Programming, 92–103. Springer, 1999.
S. Kramer, N. Lavrač and P. A. Flach. Propositionalization approaches to relational data mining. In S. Džeroski and N. Lavrač, editors, Relational Data Mining, 262–291. Springer, 2001.
M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, and J. J. Fettich. Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artificial Intelligence in Medicine, special issue on Data Mining Techniques and Applications in Medicine, 16, 25–50. Elsevier, 1998.
N. Lavrač and P. Flach. An extended transformation approach to Inductive Logic Programming. ACM Transactions on Computational Logic 2(4): 458–494, 2001.
N. Lavrač and S. Džeroski. Inductive Logic Programming: Techniques and Applications. Ellis Horwood, 1994.
N. Lavrač, P. Flach, and B. Zupan. Rule evaluation measures: A unifying view. In Proc. of the 9th International Workshop on Inductive Logic Programming, 74–185. Springer, 1999.
N. Lavrač, D. Gamberger, and V. Jovanoski. (1999). A study of relevance for learning in deductive databases. Journal of Logic Programming 40, 2/3 (August/September), 215–249.
R. S. Michalski, I. Mozetič, J. Hong, and N. Lavrač. The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proc. 5th National Conference on Artificial Intelligence, 1041–1045. Morgan Kaufmann, 1986.
S. Muggleton. Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3–4): 245–286, 1995.
F. Provost and T. Fawcett. Robust classification for imprecise environments. Machine Learning, 42(3), 203–231, 2001.
J. R. Quinlan. Learning Logical definitions from relationa. Machine Learning, 5(3): 239–266, 1990.
R. L. Rivest. Learning decision lists. Machine Learning, 2(3):229–246, 1987.
L. Todorovski, P. Flach, and N. Lavrač. Predictive performance of weighted relative accuracy. In D. A. Zighed, J. Komorowski, and J. Zytkow, editors, Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, 255–264. Springer, 2000.
S. Wrobel. An algorithm for multi-relational discovery of subgroups. In Proc. First European Symposium on Principles of Data Mining and Knowledge Discovery, 78–87. Springer, 1997.
F. Železný, J. Zídek, and O. Štěpánková. A learning system for decision support in telecommunications. In Proc. First International Conference on Computing in an Imperfect World, 88–101. Springer, 2002.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lavrač, N., Železný, F., Flach, P.A. (2003). RSD: Relational Subgroup Discovery through First-Order Feature Construction. In: Matwin, S., Sammut, C. (eds) Inductive Logic Programming. ILP 2002. Lecture Notes in Computer Science(), vol 2583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36468-4_10
Download citation
DOI: https://doi.org/10.1007/3-540-36468-4_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00567-4
Online ISBN: 978-3-540-36468-9
eBook Packages: Springer Book Archive