Abstract
Situated at the intersection of machine learning and logic programming, inductive logic programming (ILP) has been concerned with finding patterns expressed as logic programs. While ILP initially focussed on automated program synthesis from examples, it has recently expanded its scope to cover a whole range of data analysis tasks (classification, regression, clustering, association analysis). ILP algorithms can this be used to find patterns in relational data, i.e., for relational data mining (RDM). This paper briefly introduces the basic concepts of ILP and RDM and discusses some recent research trends in these areas.
Keywords
- Association Rule
- Logic Program
- Inductive Logic
- Inductive Logic Programming
- Inductive Logic Programming System
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Blockeel, H., De Raedt, L.: Top-down induction of first order logical decision trees. Artificial Intelligence 101, 285–297 (1998)
Blockeel, H., Sebag, M.: Scalability and Efficiency in Multi-Relational Data Mining. SIGKDD Explorations 5(1), 17–30 (2003)
Bratko, I.: Prolog Programming for Artificial Intelligence, 3rd edn. Addison-Wesley, Harlow (2001)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth, Belmont (1984)
de Castro Dutra, I., Page, D.L., Santos Costa, V., Shavlik, J.: An Empirical Evaluation of Bagging in Inductive Logic Programming. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS, vol. 2583, pp. 48–65. Springer, Heidelberg (2003)
Clark, P., Boswell, R.: Rule induction with CN2: Some recent improvements. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 151–163. Springer, Heidelberg (1991)
Dehaspe, L., Toivonen, H.: Discovery of frequent datalog patterns. Data Mining and Knowledge Discovery 3(1), 7–36 (1999)
Dehaspe, L., Toivonen, H.: Discovery of Relational Association Rules. In: [12], pp. 189–212 (2001)
De Raedt, L.: Logical settings for concept learning. Artificial Intelligence 95, 187–201 (1997)
De Raedt, L., Džeroski, S.: First order jk-clausal theories are PAC-learnable. Artificial Intelligence 70, 375–392 (1994)
De Raedt, L., Kersting, K.: Probabilistic Logic Learning. SIGKDD Explorations 5(1), 31–48 (2003)
Džeroski, S., Lavrač, N. (eds.): Relational Data Mining. Springer, Berlin (2001)
Džeroski, S., Blockeel, H., Kompare, B., Kramer, S., Pfahringer, B., Van Laer, W.: Experiments in Predicting Biodegradability. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS, vol. 1634, pp. 80–91. Springer, Heidelberg (1999)
Džeroski, S.: Relational Data Mining Applications: An Overview. In: [12], pp. 339–364 (2001)
Džeroski, S., De Raedt, L. (eds): Special Issue on Multi-Relational Data Mining. SIGKDD Explorations 5(1) (2003)
Emde, W., Wettschereck, D.: Relational instance-based learning. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 122–130. Morgan Kaufmann, San Mateo (1996)
Gaertner, T.: Kernel-based Learning in Multi-Relational Data Mining. SIGKDD Explorations 5(1), 49–58 (2003)
Horváth, T., Wrobel, S., Bohnebeck, U.: Relational instance-based learning with lists and terms. Machine Learning 43(1-2), 53–80 (2001)
Karalič, A., Bratko, I.: First order regression. Machine Learning 26, 147–176 (1997)
Kirsten, M., Wrobel, S., Horváth, T.: Distance Based Approaches to Relational Learning and Clustering. In: [12], pp. 213–232 (2001)
Kramer, S.: Structural regression trees. In: Proceedings of the Thirteenth National Conference on Artificial Intelligence, pp. 812–819. MIT Press, Cambridge (1996)
Lavrač, N., Džeroski, S., Grobelnik, M.: Learning nonrecursive definitions of relations with LINUS. In: Kodratoff, Y. (ed.) EWSL 1991. LNCS, vol. 482, pp. 265–281. Springer, Heidelberg (1991)
Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood, Chichester (1994), Freely available at: http://www-ai.ijs.si/SasoDzeroski/ILPBook/
Muggleton, S.: Inductive logic programming. New Generation Computing 8(4), 295–318 (1991)
Muggleton, S.: Inverse entailment and Progol. New Generation Computing 13, 245–286 (1995)
Muggleton, S., Buntine, W.: Machine invention of first-order predicates by inverting resolution. In: Proceedings of the Fifth International Conference on Machine Learning, pp. 339–352. Morgan Kaufmann, San Mateo (1988)
Muggleton, S., Feng, C.: Efficient induction of logic programs. In: Proceedings of the First Conference on Algorithmic Learning Theory, Ohmsha, Tokyo, pp. 368–381 (1990)
Nedellec, C., Rouveirol, C., Ade, H., Bergadano, F., Tausend, B.: Declarative bias in inductive logic programming. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 82–103. IOS Press, Amsterdam (1996)
Quinlan, R.: Relational Learning and Boosting. In: [12], pp. 292–306 (2001)
Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5(3), 239–266 (1990)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
Shapiro, E.: Algorithmic Program Debugging. MIT Press, Cambridge (1983)
Srinivasan, A.: The Aleph Manual. Technical Report, Computing Laboratory, Oxford University (2000), Available at: http://web.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/
Van Laer, V., De Raedt, L.: How to Upgrade Propositional Learners to First Order Logic: A Case Study. In: [12], pp. 235–261 (2001)
Vens, C., Van Assche, A., Blockeel, H., Džeroski, S.: First order random forests with complex aggregates. In: Camacho, R., King, R., Srinivasan, A. (eds.) ILP 2004. LNCS, vol. 3194, pp. 323–340. Springer, Heidelberg (2004)
Wrobel, S., Džeroski, S.: The ILP description learning problem: towards a general model-level definition of data mining in ILP. In: Proceedings Fachgruppentreffen Maschinelles Lernen, University of Dortmund, Germany (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Džeroski, S. (2006). From Inductive Logic Programming to Relational Data Mining. In: Fisher, M., van der Hoek, W., Konev, B., Lisitsa, A. (eds) Logics in Artificial Intelligence. JELIA 2006. Lecture Notes in Computer Science(), vol 4160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11853886_1
Download citation
DOI: https://doi.org/10.1007/11853886_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39625-3
Online ISBN: 978-3-540-39627-7
eBook Packages: Computer ScienceComputer Science (R0)