Evolving SQL queries for data mining

Majid SALIM, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

6 Citations (Scopus)


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. © Springer-Verlag Berlin Heidelberg 2002.
Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning : IDEAL 2002 : Third International Conference, Manchester, UK, August 12-14 Proceedings
EditorsHujun YIN, Nigel ALLINSON, Richard FREEMAN, John KEANE, Simon HUBBARD
PublisherSpringer Berlin Heidelberg
Number of pages6
ISBN (Electronic)9783540456759
ISBN (Print)9783540440253
Publication statusPublished - 2002
Externally publishedYes
Event3rd International Conference on Intelligent Data Engineering and Automated Learning - Manchester, United Kingdom
Duration: 12 Aug 200214 Aug 2002

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Berlin, Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated titleIDEAL'02
Country/TerritoryUnited Kingdom


  • Data Mining
  • Evolutionary Algorithm
  • Genetic Programming
  • Evolutionary Computation
  • Credit Card Fraud


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