Advertisement

Evolving SQL Queries for Data Mining

  • Majid Salim
  • Xin Yao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

This paper presents a methodology for applying the principles of evolutionary computation to knowledge discovery in databases by evolving SQL queries that describe datasets. In our system, the fittest queries are rewarded by having their attributes being given a higher probability of surviving in subsequent queries. The advantages of using SQL queries include their readability for non-experts and ease of integration with existing databases. The evolutionary algorithm (EA) used in our system is very different from existing EAs, but seems to be effective and efficient according to the experiments to date with three different testing data sets.

Keywords

Data Mining Evolutionary Algorithm Genetic Programming Evolutionary Computation Credit Card Fraud 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    X. Yao and Y. Liu,’ A new evolutionary system for evolving artificial neural networks,’ IEEE Transactions on Neural Networks, 8(3):694–713, May 1997.Google Scholar
  2. 2.
    X. Yao and Y. Liu,’ Making use of population information in evolutionary artificial neural networks,’ IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 28(3):417–425, June 1998.Google Scholar
  3. 3.
    Y. Liu, X. Yao and T. Higuchi,’ Evolutionary ensembles with negative correlation learning,’ IEEE Transactions on Evolutionary Computation, 4(4):380–387, November 2000.Google Scholar
  4. 4.
    J. Bobbin and X. Yao,’ Evolving rules for nonlinear control’, In New Frontier in Computational Intelligence and its Applications, M. Mohammadian (ed.), IOS Press, Amsterdam, 2000, pp. 197–202.Google Scholar
  5. 5.
    A. A. Freitas,’ A genetic programming framework for two data mining tasks: classification and knowledge discovery’, Genetic Programming 1997: Proc. 2nd Annual Conference, pp 96–101, Stanford University, 1997Google Scholar
  6. 6.
    A. A. Freitas,’ A survey of evolutionary algorithms for data mining and knowledge discovery’, In: A. Ghosh, S. Tsutsui (eds.), Advances in Evolutionary Computation, Springer-Verlag, 2001Google Scholar
  7. 7.
    T. W. Ryu, C. F. Eick,’ Deriving queries from results using genetic programming’, Proc. 2nd International Conference, Knowledge Discovery and Data Mining, pp 303–306, AAAI Press, 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Majid Salim
    • 1
  • Xin Yao
    • 1
  1. 1.School of Computer ScienceThe University of BirminghamEdgbastonUK

Personalised recommendations