A comparative study on ILP-based concept discovery systems

Expert Systems with Applications

Volumn 38, Issue 9, 2011, Pages 11598-11607

  Kavurucu, Y ^a   Senkul, P ^a   Toroslu, I H ^a  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Concept discovery; ILP; Multi relational data mining

Indexed keywords

CLAUSAL LOGIC; COMPARATIVE STUDIES; CONCEPT DISCOVERY; DISCOVERY SYSTEMS; ILP; LANGUAGE PATTERNS; LEARNING FROM EXAMPLES; MULTIRELATIONAL DATA MINING; REAL-WORLD PROBLEM; SEARCH STRATEGIES;

COMPUTER PROGRAMMING; DATA MINING;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 79955627413     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.03.038     Document Type: Article

References (29)

1. Agrawal, R.; Mannila, H.; Srikant, R.; Toivonen, H.; Verkamo, A. I. (1996). Fast discovery of association rules. In Advances in knowledge discovery and data mining (pp. 307-328) AAAI/MIT Press.
2. Dehaspe, L.; Raedt, L. D. (1997). Mining association rules in multiple relations. In ILP'97: Proceedings of the 7th international workshop on inductive logic programming (pp. 125-132.) London, UK: Springer-Verlag. (Pubitemid 127124383)
3. L. Dehaspe, and H. Toivonen Discovery of relational association rules S. Džeroski, N. Lavrač, Relational data mining 2001 Springer-Verlag 189 212
4. B. Dolsak, and S. Muggleton The application of inductive logic programming to finite element mesh design S. Muggleton, Inductive logic programming 1992 Academic Press London www.citeseer.ist.psu.edu/ dolsak92application.htm
5. P. Domingos Prospects and challenges for multi-relational data mining SIGKDD Explorations 5 2003 80 81
6. S. Džeroski Multi-relational data mining: An introduction SIGKDD Explorations 5 1 2003 1 16
7. S. Dzeroski Relational Data Mining 2001 Springer-Verlag New York, Inc. Secaucus, NJ, USA
8. Dzeroski, S. (2006). From inductive logic programming to relational data mining. In logics in artificial intelligence (pp. 1-14). (Pubitemid 44576952)
9. Kavurucu, Y.; Senkul, P.; Toroslu, I. H. (2008a). Aggregation in confidence-based concept discovery for multi-relational data mining. In proceedings of IADIS European conference on data mining (ECDM) (pp. 43-50). Netherland: Amsterdam.
10. Kavurucu, Y.; Senkul, P.; Toroslu, I. H.; March 2008b. Confidence-based concept discovery in multi-relational data mining. In proceedings of international conference on data mining and applications (ICDMA) (pp. 446-451). Hong Kong.
11. Kavurucu, Y.; Senkul, P.; Toroslu, I. H. (2009a). Analyzing transitive rules on a hybrid concept discovery system. In: LNCS, hybrid artificial intelligent systems (Vol. 5572/2009, pp. 227-234). Springer Berlin/Heidelberg.
12. Y. Kavurucu, P. Senkul, and I.H. Toroslu ILP-based concept discovery in multi-relational data mining Expert Systems with Applications 36 9 2009 11418 11428
13. Knobbe, A. J. (2004). Multi-relational data mining. Ph.D. thesis, Utrecht, Netherlands: Utrecht University.
14. N. Lavrač, and S. Džeroski Inductive logic programming: Techniques and applications 1994 Ellis Horwood New York
15. Lavrač, N.; Džeroski, S.; Grobelnik, M. (1991). Learning nonrecursive definitions of relations with LINUS. In Y. Kodratoff (Ed.), proceedings of the 5th european working session on learning (Vol. 482, pp. 265-281.) Lecture Notes in Artificial Intelligence. Springer-Verlag.
16. Michalski, R.; Larson, J. (1997). Inductive inference of VL decision rules. In workshop on pattern-directed inference systems (Vol. 63, pp. 33-44.). SIGART Newsletter, ACM, Hawaii.
17. Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming 13(3-4) (pp. 245-286).
18. Muggleton, S.; 1999. Inductive Logic Programming. In The MIT encyclopedia of the cognitive sciences (MITECS). MIT Press.
19. Muggleton, S.; Buntine, W. (1988). Machine invention of first order predicates by inverting resolution. In Proceedings of the 5th international workshop on machine learning (pp. 339-351). Morgan Kaufmann.
20. Muggleton, S.; Feng, C. (1990). Efficient induction of logic programs. In proceedings of the first conference on algorithmic learning theory (pp. 368-381). Tokyo, Japan: Ohmsma, www.citeseer.ist.psu.edu/muggleton90efficient. html.
21. S. Muggleton, and A. Tamaddoni-Nezhad QG/GA: A stochastic search for progol Machine Learning 70 2-3 2008 121 133
22. J.R. Quinlan Learning logical definitions from relations Machine Learning 5 3 1990 239 266
23. E. Shapiro Algorithmic Program Debugging 1983 The MIT Press
24. Srinivasan, A. (1999). The aleph manual.
25. A. Srinivasan, R. King, and S. Muggleton The role of background knowledge: Using a problem from chemistry to examine the performance of an ILP program Under review for intelligent data analysis in medicine and pharmacology 1996 Kluwer Academic Press www.citeseer.ist.psu.edu/srinivasan96role.html
26. Srinivasan, A.; King, R. D.; Muggleton, S.; Sternberg, M. J. E. (1997). Carcinogenesis predictions using ILP. In Proceedings of the 7th international workshop on inductive logic programming (Vol. 1297, pp. 273-287). Springer-Verlag (citeseer.ist.psu.edu/srinivasan97carcinogenesis.html ). (Pubitemid 127124399)
27. Srinivasan, A.; Muggleton, S.; King, R.; Sternberg, M. (1995). Theories for mutagenecity: A study of first-order and feature based induction. Tech. rep.; PRG-TR-8-95 Oxford University Computing Laboratory.
28. M. Yankova, and S. Boytcheva Overview of inductive logic programming (ILP) systems 2002 345 359
29. Yin, X.; Han, J.; Yang, J.; Yu, P. S. (2004). Crossmine: Efficient classification across multiple database relations. In ICDE (pp. 399-411).