Using Logical Decision Trees for Clustering

Luc De Raedt and Hendrik Blockeel

Department of Computer Science, Katholieke Universiteit Leuven,
Celestijnenlaan 200A, B-3001 Heverlee, Belgium
email: {Luc.DeRaedt,Hendrik.Blockeel}@cs.kuleuven.ac.be


Abstract. A novel first order clustering system, called C 0.5, is presented. 
It inherits its logical decision tree formalism from the TILDE
system, but instead of using class information to guide the search, it
employs the principles of instance based learning in order to perform
clustering. Various experiments are discussed, which show the promise
of the approach.
References

1.	G. Bisson. Conceptual clustering in a first order logic representation. In Proceedings 
of the 10th European Conference on Artificial Intelligence, pages 458462.
John Wiley & Sons, 1992.
2.	H. Blockeel and L. De Raedt. Experiments with top-down induction of logical
decision trees. Technical Report OW 247, Dept. of Computer Science, K.U.Leuven,
January 1997. Also in Periodic Progress Report ESPRIT Project ILP2, January
1997.
3.	L. De Raedt. Induction in logic. In R.S. Michalski and Wnek J., editors, Proceedings 
of the 3rd International Workshop on Multistrategy Learning, pages 2938,
1996.
4.	L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning, 26:99146,
1997.
5.	L. De Raedt and S. Dzeroski. First order jk-clausal theories are PAC-learnable.
Artificial Intelligence, 70:375392, 1994.
6.	L. De Raedt and W. Van Laer. Inductive constraint logic. h Proceedings of the
5th Workshop on Algorithmic Learning Theory, volume 997 of Lecture Notes in
Artificial Intelligence. Springer-Verlag, 1995.
7.	W. Emde. Inductive learning of characteristic concept descriptions. In S. Wrobel,
editor, Proceedings of the 4th International Workshop on Inductive Logic Programming, 
volume 237 of GMD-Studien, pages 5170, Sankt Augustin, Germany, 1994.
Gesellschaft fr Mathematik und Datenverarbeitung MBH.
8.	W. Emde and D. Wettschereck. Relational instance-based learning. In L. Saitta,
editor, Proceedings of the 13th International Conference on Machine Learning,
pages 122130. Morgan Kaufmann, 1996.
9.	D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine 
Learning, 2:139172, 1987.
10.	S. Kramer. Structural regression trees. In Proceedings of the 13th National Conference 
on Artificial Intelligence (AAAI-96), 1996.
11.	P. Langley. Elements of Machine Learning. Morgan Kaufmann, 1996.
12.	G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5,
pages 153163. Edinburgh University Press, 1970.
13.	L. De Raedt, P. Idestam-Almquist, and G. Sablon. Theta-subsumption for structural 
matching. In Proceedings of the 9th European Conference on Machine Learning, 1997.
14.	A. Srinivasan, S.H. Muggleton, and R.D. King. Comparing the use of background
knowledge by inductive logic programming systems. In L. De Raedt, editor, Proceedings 
of the 5th International Workshop on Inductive Logic Programming, 1995.
