Discovering relational item sets efficiently

Society for Industrial and Applied Mathematics - 8th SIAM International Conference on Data Mining 2008, Proceedings in Applied Mathematics 130

Volumn 1, Issue , 2008, Pages 108-119

  Koopman, Arne ^a   Siebes, Arno ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

Frequent patterns; MDL; Relational mining

Indexed keywords

EFFECTIVE ALGORITHMS; FREQUENT ITEM SETS; FREQUENT PATTERNS; ITEM SETS; ORDERS OF MAGNITUDE; SEARCH SPACES; SET MINING;

DATA MINING;

EID: 52649132363     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972788.10     Document Type: Conference Paper

References (13)

1. Rakesh Agrawal, Tomasz Imielinski, and Aran N. Swami.: Mining association rules between sets of items in large databases. In: Buneman, P. & Jajodia, S., (Eds), Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data. (1993)(207-216).
2. Dehaspe, L. & Toivonen, H. : Discovery of Relational Association Rules. In: Ḋzeroski, S. & Lavrac, N., Relational Data Mining. Springer-Verlag. (2001)(189-212).
3. Grunwald, P. D. Minimum Description Length tutorial. In: Grunwald, P.D., Myung, I.J. & Pitt, M.A. (eds.) Advances in Minimum Description Length. MIT Press, 2005.
4. Faloutsos, C. & Megalooikonomou, V. On data mining, compression, and Kolomogorov Complexity. In: Data Mining and Knowledge Discovery, 15(1), pp.3-20, 2007.
5. Han, J., Pei J, & Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: ICMD. (2000) (1-12)
6. Knobbe, A.J.: Multi-Relational Data Mining. Frontiers in Artificial Intelligence and Applications. IOSPress. (2006).
7. Leeuwen, M. van, Vreeken, J., & Siebes, A.P.J.M.: Compression Picks Item Sets That Matter. In J. Furnkranz, T. Scheffer, & M. Spiliopoulou (eds.), Knowledge Discovery in Databases: PKDD. Springer. (2006) (585-592).
8. Li, M. & Vitányi, P.M.B. An introduction to Kolmogorov complexity and its applications. Springer-Verlag, 1993.
9. Ramakrishnan, R. & Gehrke J. Database Management Systems. McGraw-Hill (1998).
10. Siebes, A.P.J.M., Vreeken, J., & Leeuwen, M. van: Item Sets that Compress. In: J. Ghosh, D. Lambert, D.B. Skillicorn, & J. Srivastava (eds.): SIAM Conference on Data Mining. SIAM. (2006) (393-404).
11. Vreeken, J., Leeuwen, M. & Siebes, A.P.J.M.: Characterising the Difference. In: P. Berkhin, R. Caruana, X. Wu, & S. Gaffney (eds.); Conference on Knowledge Discovery in Data. KDD. (2007) (765-774).
12. Wallace, C.S. Statistical and inductive inference by minimum message length. Springer-Verlag, 2005.
13. Yin, X., Han, J., Yang, J., & Yu, P.: Crossmine: Efficient classification across multiple database relations. In: Proceedings of the International Conference on Data Engineering (ICDE'04), (2004)