Skip to main content

Using logical decision trees for clustering

  • Part II Papers
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1297))

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.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. G. Bisson. Conceptual clustering in a first order logic representation. In Proceedings of the 10th European Conference on Artificial Intelligence, pages 458–462. John Wiley & Sons, 1992.

    Google Scholar 

  2. H. Blockeel and L. De Raedt. Experiments with top-down induction of logical decision trees. Technical Report CW 247, Dept. of Computer Science, K.U.Leuven, January 1997. Also in Periodic Progress Report ESPRIT Project ILP2, January 1997.

    Google Scholar 

  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 29–38, 1996.

    Google Scholar 

  4. L. De Raedt and L. Dehaspe. Clausal discovery. Machine Learning, 26:99–146, 1997.

    Google Scholar 

  5. L. De Raedt and S. Dieroski. First order jk-Causal theories are PAC-learnable. Artificial Intelligence, 70:375–392, 1994.

    Google Scholar 

  6. L. De Raedt and W. Van Laer. Inductive constraint logic. In Proceedings of the 5th Workshop on Algorithmic Learning Theory, volume 997 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1995.

    Google Scholar 

  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 51–70, Sankt Augustin, Germany, 1994. Gesellschaft für Mathematik und Datenverarbeitung MBH.

    Google Scholar 

  8. W. Emde and D. Wettschereck. Relational instance-based learning. In L. Saitta, editor, Proceedings of the 13th International Conference on Machine Learning, pages 122–130. Morgan Kaufmann, 1996.

    Google Scholar 

  9. D. H. Fisher. Knowledge acquisition via incremental conceptual clustering. Machine Learning, 2:139–172, 1987.

    Google Scholar 

  10. S. Kramer. Structural regression trees. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-98), 1996.

    Google Scholar 

  11. P. Langley. Elements of Machine Learning. Morgan Kaufmann, 1996.

    Google Scholar 

  12. G. Plotkin. A note on inductive generalization. In Machine Intelligence, volume 5, pages 153–163. Edinburgh University Press, 1970.

    Google Scholar 

  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.

    Google Scholar 

  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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Nada Lavrač Sašo Džeroski

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

De Raedt, L., Blockeel, H. (1997). Using logical decision trees for clustering. In: Lavrač, N., Džeroski, S. (eds) Inductive Logic Programming. ILP 1997. Lecture Notes in Computer Science, vol 1297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540635149_41

Download citation

  • DOI: https://doi.org/10.1007/3540635149_41

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63514-7

  • Online ISBN: 978-3-540-69587-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics