Aggregation in confidence-based concept discovery for multi-relational data mining

MCCSIS'08 - IADIS Multi Conference on Computer Science and Information Systems; Proceedings of Informatics 2008 and Data Mining 2008

Volumn , Issue , 2008, Pages 43-50

  Kavurucu, Yusuf ^a   Senkul, Pinar ^a   Toroslu, Ismail Hakki ^a  

^a MIDDLE EAST TECHNICAL UNIVERSITY   (Turkey)

Author keywords

Aggregate predicates; Data mining; ILP; MRDM

Indexed keywords

AGGREGATES; COMPUTERS; INFORMATION MANAGEMENT; INFORMATION SYSTEMS;

AGGREGATE PREDICATES; DATA MINING; EVALUATION CRITERIONS; ILP; KNOWLEDGE DISCOVERY (KDD); MRDM; MULTI RELATIONAL DATA MINING; PROBLEM DEFINITION; SEARCH SPACES; SEARCH STRATEGIES;

KNOWLEDGE MANAGEMENT;

EID: 58449085819     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Bayardo, R. J., et al., 2000. Constraint-based Rule Mining in Large, Dense Databases. In Data Mining and Knowledge Discovery, Vol: 4(2-3), pp.217-240.
2. Dehaspe, L. and Raedt, L.D., 1997. Mining Association Rules in Multiple Relations. In Proceedings of the 7th International Workshop on Inductive Logic Programming, London, UK, Springer-Verlag, pp. 125-132.
3. Dzeroski, S., 2003. Multi-relational Data Mining: An Introduction. In SIGKDD Explorations, Vol: 5(1), pp. 1-16.
4. Frank, R., et al., 2007. A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions. In Proceedings of Principles of Knowledge Discovery in Databases (PKDD). pp. 430-437.
5. Getoor, L. and Grant, J., 2006. PRL: A Probabilistic Relational Language. In Machine Learning, Vol: 62(1-2), pp. 7-31.
6. Kavurucu, Y., et al., 2008. Confidence-based Concept Discovery in Multi-relational Data mining. In: IAENG International Conference on Data Mining and Applications (ICDMA). Hong Kong, China.
7. Knobbe, A., et al., 2002. Involving Aggregate Functions in Multi-relational Search. In Proceedings of Principles of Knowledge Discovery in Databases (PKDD). pp. 287-298.
8. Knobbe, A.J., 2004. Multi-Relational Data Mining. PhD thesis, Utrecht University, Utrecht, Netherlands.
9. Leiva, H.A., 2002. MRDTL: A Multi-Relational Decision Tree Learning Algorithm. Master's Thesis. Iowa State University.
10. Muggleton, S., 1995. Inverse Entailment and Progol. In New Generation Computing, Special Issue on Inductive Logic Programming, Vol: 13(3-4), pp.245-286.
11. Muggleton, S., 1999. Inductive Logic Programming. In The MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press.
12. Neville, J., et al., 2003. Learning Relational Proability Trees. In Proceedings of Knowledge Discovery in Databases.
13. Perlich, C., and Provost, F., 2003. Aggregation-Based Feature Invention and Relational Concept Classes. In Proceedings of Knowledge Discovery in Databases (KDD). Washington. DC, pp. 167-176.
14. Quinlan, J.R., 1990. Learning Logical Definitions from Relations. In Machine Learning. Vol: 5(3), pp.239-266.
15. Srinivasan, A., et al., 1995. Theories for Mutagenecity: A Study of First-order and Feature based Induction. Technical report, PRG-TR-8-95, Oxford University Computing Laboratory.
16. Srinivasan, A., et al., 1996. The Role of Background Knowledge: Using a Problem from Chemistry to Examine the Performance of an ILP Program, In Intelligent Data Analysis in Medicine and Pharmacology. Kluwer Academic Press.
17. Srinivasan, A., et al., 1997. Carcinogenesis Predictions using ILP. In Proceedings of the 7th International Workshop on Inductive Logic Programming. Vol: 1297., Springer-Verlag, pp.273-287.
18. Srinivasan, A., 1999. The Aleph Manual.
19. Toprak, S.D., et al., 2007. A New ILP-based Concept Discovery Method for Business Intelligence. In ICDE Workshop on Data Mining and Business Intelligence.
20. Van Aasche, et al., 2006. First Order Random Forests: Learning Relational Classifiers with Complex Aggregates. Machine Learning, Special Issue on ILP.
21. Yin, X., et al., 2004. Crossmine: Efficient Classification Accross Multiple Database Relations. In : Proceedings of ICDE.