Abstract
In this paper, we present a Data Mining tool based on Genetic Programming which enables to analyze complex databases, involving several relation schemes. In our approach, trees represent expressions of relational algebra and they are evaluated according to the way they discriminate positive and negative examples of the target concept. Nevertheless, relational algebra expressions are strongly typed and classical genetic operators, such as mutation and crossover, have been modified to prevent from building illegal expressions. The Genetic Programming approach that we have developed has been modeled in the framework of constraints.
Chapter PDF
References
[AVK95] S. Augier, G. Venturini, and Y. Kodratoff. Learning first order logic rules with a genetic algorithm. In Proceedings of the First International Conference on Knowledge Discovery & Data Mining (KDD’95), pages 21–26, Canada, August 1995.
[BR97] Hendrik Blockeel and Luc De Raedt. Relational knowledge discovery in databases. In Stephen Muggleton, editor, Proceedings of the 6th International Workshop on Inductive Logic Programming (ILP-96), volume 1314 of LNAI, pages 199–211, Berlin, August 26–28 1997. Springer.
[FPSSU96] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors. Advances in Knowledge Discovery and Data Mining. MIT Press, Mento Park, 1996.
[GSB97] A. Giordana, L. Saitta, and G. Lo Bello. A coevolutionary approach to concept learning. In Zbigniew W. Raś and Andrzej Skowron, editors, Proceedings of the 10th International Symposium on Foundations of Intelligent Systems (ISMIS-97), volume 1325 of LNAI, pages 257–266, Berlin, October 15–18 1997. Springer.
[Koz92] J. R. Koza. Genetic Programming On the programming of computers by means of natural selection. MIT Press, Cambridge, Massachusetts, 1992.
[Koz94] J. R. Koza. Genetic Programming II Automatic Discovery of Reusable Programs. MIT Press, Cambridge, Massachusetts, 1994.
[MMPS94] D. Michie, S. Muggleton, D. Page, and A. Srinivasan. To the international computing community: A new East-West challenge. Technical report, Oxford University Computing laboratory, Oxford, UK, 1994.
[Mon95] David J. Montana. Strongly typed genetic programming. Evolutionary computation, 3(2):199–230, 1995.
[RE96] Tae-Wan Ryu and Christoph F. Eick. Deriving queries from examples using genetic programming. In Evangelos Simoudis, Jia Wei Han, and Usama Fayyad, editors, The Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pages 303–306, Portland, Oregon, USA, August 2–4 1996. AAAI.
[Sch87] J. C. Schlimmer. Concept acquisition through representational adjustment. Technical Report ICS-TR-87-19, University of California, Irvine, Department of Information and Computer Science, July 1987.
[SJB+93] W. M. Spears, K. A. De Jong, T. Bäck, D. B. Fogel, and H. de Garis. An overview of evolutionary computation. In Pavel B. Brazdil, editor, Proceedings of the European Conference on Machine Learning (ECML-93), volume 667 of LNAI, pages 442–459, Vienna, Austria, April 1993. Springer Verlag.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martin, L., Moal, F., Vrain, C. (1998). A relational data mining tool based on genetic programming. In: Żytkow, J.M., Quafafou, M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0094813
Download citation
DOI: https://doi.org/10.1007/BFb0094813
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65068-3
Online ISBN: 978-3-540-49687-8
eBook Packages: Springer Book Archive