Mining Association Rules in Multiple Relations

Luc Dehaspe and Luc De Raedt

Katholieke Universiteit Leuven, Department of Computer Science,
Celestijnenlaan 200A, B-3001 Heverlee, Belgium
email : {Luc.Dehaspe, Luc.DeRaedt}@cs.kuleuven.ac.be
fax : ++ 32 16 32 79 96; telephone : ++ 32 18 32 75 67



Abstract. The application of algorithms for efficiently generating association 
rules is so far restricted to cases where information is put together
in a single relation. We describe how this restriction can be overcome
through the combination of the available algorithm. with standard techniques 
from the field of inductive logic programming. We present the
system WARMR, which extends APRIORI [2] to mine association rules
in multiple relations. We apply WARMR to the natural language processing 
task of mining part-of-speech tagging rules in a large corpus of
English. be applied to further constrain the space of interesting ARMRs.
References

1.	R.. Agrawal, T. Imielinski, and A. Swami. Mining assosiation rules between sets of
items in large databases. In Proceedings of the 1993 International Conference on
Management of Data (SIGMOD 93), pages 207216, May 1993.
2.	R.. Agrawal, H. Mannila, R.. Srikant, H. Toivonen, and A.I. Verkamo. Fast discovery 
of association rules. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and
R.. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages
307328. The MIT Press, 1996.
3.	L. De Raedt, editor. Advances in Inductive Logic Programming, volume 32 of Frontiers 
in Artificial Intelligence and Applications. IOS Press, 1996.
4.	L. De Raedt. Induction in logic. In R.S. Michaiski and Wnek J., editors, Proceedings 
of the 3rd International Workshop on Multistrategy Learning, pages 2938,
1996.
5.	L. De Raedt and S. Dzeroski. First order jk-clausal theories are PAC-learnable.
Artificial Intelligence, 70:375392, 1994.
6.	5. Dzeroski. Inductive logic programming and knowledge discovery in databases.
In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R.. Uthurusamy, editors, Advances 
in Knowledge Discovery and Data Mining, pages 118152. The MIT Press,
1996.
7.	M. Houtsma and A. Swami. Set-oriented mining of assocation rules. Technical
Report RJ 9567, IBM Almaden Research Center, San Jose, Calif., 1993.
8.	N. Lavrac and S. Dzeroski. Inductive Logic Programming: Techniques and Applications.
 Ellis Horwood, 1994.
9.	M.P. Marcus, B. Santorini, and M. A. Marcinkiewics. Building a large annotated
corpus of English: the Penn Treebank. Computational Linguistics, 19(2):313330,
1993.
10.	G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5,
pages 153163. Edinburgh University Press, 1970.
