Post-processing of discovered association rules using ontologies

Proceedings - IEEE International Conference on Data Mining Workshops, ICDM Workshops 2008

Volumn , Issue , 2008, Pages 126-133

  Marinica, Claudia ^a   Guillet, Fabrice ^a   Briand, Henri ^a  

^a UNIVERSITÉ DE NANTES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION RULES; ASSOCIATIVE PROCESSING; KNOWLEDGE MANAGEMENT; TECHNICAL PRESENTATIONS;

DOMAIN ONTOLOGIES; ITERATIVE FRAMEWORKS; NEW APPROACHES; NOVEL TECHNIQUES; POST-PROCESSING; USER DOMAINS;

ONTOLOGY;

EID: 62449181291     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDMW.2008.87     Document Type: Conference Paper

References (20)

1. Adomavicius, G. and A. Tuzhilin. (2001). Expert-Driven Validation of Rule-Based User Models in Personalization Applications. Data Mining and Knowledge Discovery, pages 33-58.
2. Agrawal, R., T. Imielinski, and A. Swami. (1993). Mining Association Rules between Sets of Items in Large Databases. Proceedings of the 12th ACM SIGMOD International Conference on Management of Data, pages 207-216.
3. An, A., S. Khan, and X. Huang. (2003). Objective and Subjective Algorithms for Grouping Association Rules. Internation Conference in Data Mining, pages 477-480.
4. Baesens, B., S. Viaene, and J. Vanthienen. (2000). Post-Processing of Association Rules. The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'2000), pages 2-8.
5. Berrado, A. and G. C. Runger. (2007). Using metarules to organize and group discovered association rules. Data Mining and Knowledge Discovery, pages 409-431.
6. Bayardo, R.J. Jr. and R. Agrawal. (1999). Mining the most interesting rules. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, 145-154.
7. Češpivová, H., J. Rauch, V. Svátek, M. Kejkula, and M. Tomečková. (2004). Roles of Medical Ontology in Association Mining CRISP-DM Cycle. Workshop Knowledge Discovery and Ontologies in ECML/PKDD
8. Chawla, S., J. Davis, and G. Pandey. (2004). On Local Pruning of Association Rules Using Directed Hypergraphs. Proceedings of the 20th International Conference on Data Engineering, pages 832.
9. Euler, T. and M. Scholz. (2004) Using Ontologies in a KDD Workbench. In Workshop on Knowledge Discovery and Ontologies at ECML/PKDD.
10. Fayyad, U. M., G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. (1996) Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press.
11. Gruber, T. (1993). A translation approach to portable ontology specification. Knowledge Acquisition, 5:199-220.
12. Klemettinen, M., H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. (1994). Finding Interesting Rules from Large Sets of Discovered Association Rules. International Conference on Information and Knowledge Management (CIKM), pages 401-407.
13. Liu, B., W. Hsu, K. Wang and S. Chen. (1999). Visually Aided Exploration of Interesting Association Rules. Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining, Lecture Notes In Computer Science, Vol. 1574, Springer-Verlag, pages 26-28.
14. Nigro, H.O., S.E. Gonzalez Cisaro, and D.H. Xodo. (2007) Data Mining With Ontologies: Implementations, Findings and Frameworks, Idea Group Reference.
15. Padmanabhan, B. and A. Tuzhuilin. (1999). Unexpectedness as a Measure of Interestingness in Knowledge Discovery. Decision Support Systems, Volume 27, Number 3, Elsevier, pages 303-318.
16. Piatetsky-Shapiro, G. and C.J. Matheus. (1994). The Interestingness of Deviations. In U. M. Fayyad and R. Uthurusamy (eds.), Knowledge Discovery in Databases, Papers from AAAI Workshop (KDD'94), pages 25-36.
17. Silberschatz, A. and A. Tuzhilin. (1996). What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transactions on Knowledge and Data Engineering, IEEE Educational Activities Department, pages 970-974.
18. Srikant, R. and R. Agrawal. (1995). Mining Generalized Association Rules. In U. Dayal, P.M.D. Gray, and S. Nishio, eds, Proceedings of the 21st International Conference on Very Large Databases, pages 407-419.
19. Toivonen, H., M. Klementinen, P. Ronkainen, K. Hatonen, and H. Mannila. (1995). Pruning and Grouping of Discovered Association Rules. Mlnet Workshop on Statistics, Machine Learning, and Discovery in Databases, pages 47-52.
20. Zhou, X. and J. Geller. (2007). Raising, to enhance rule mining in web marketing with the use of an ontology. Date Mining with Ontologies: Implementations, Findings and Frameworks, pages 18-36.