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Using genetic algorithms to mine interesting dependence modeling rules

Goncalves, A.S. and Freitas, Alex A. and Kato, R. and de Oliveira, R.C.L. (2005) Using genetic algorithms to mine interesting dependence modeling rules. In: Hamza, M.H., ed. Databases and Applications. ACTA Press, pp. 1-6. ISBN 0-88986-460-8. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:14364)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)

Abstract

The challenge of KDD (Knowledge Discovery in Databases) is to efficiently and automatically analyze the available information, extracting useful knowledge from databases. We present GenMiner - a Data Mining tool for the Dependence Modeling task. GenMiner is a genetic algorithm based tool that searches for interesting rules involving correlated attributes on a relational database. Generated rules are evaluated on an individual basis, favoring accurate and surprising rules. The genetic individual encoding provided relational database integration. Chromosomes in our GA are represented by SQL queries. GenMiner evaluation was based on a public domain database including information about applications for nursery schools.

Item Type: Book section
Uncontrolled keywords: genetic algorithms, relational databases, data mining
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:03 UTC
Last Modified: 16 Feb 2021 12:25 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14364 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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