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A Parallel Genetic Algorithm for Rule Discovery in Large Databases

Alves de Araujo, Dieferson L. and Lopes, Heitor S. and Freitas, Alex A. (1999) A Parallel Genetic Algorithm for Rule Discovery in Large Databases. In: Ilto, K., ed. 1999 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, pp. 940-945. ISBN 0-7803-5734-5. (doi:10.1109/ICSMC.1999.823354) (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:21764)

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)
Official URL
http://dx.doi.org/10.1109/ICSMC.1999.823354

Abstract

This paper presents GA-PVMINER, a parallel genetic algorithm that uses the Parallel Virtual Machine (PVM) to discover rules in a database. The system uses the Michigan's approach, where each individual represents a rule. A rule has the form "if condition then prediction". GA-PVMINER is based on the concept learning framework, but it performs a generalization of the classification task, which can be called dependence modeling (sometimes also called generalized rule induction). In this task, different discovered rules can predict the value of different goal attributes in the "prediction" part of a rule, whereas in classification all discovered rules predict the value of the same goal attribute. The global population of genetic algorithm individuals is divided into several subpopulations, each assigned to a distinct processor. For each subpopulation, all the individuals represent rules with the same goal attribute in the "prediction" part of the rule. Different subpopulations evolve rules predicting different goal attributes. The system exploits both data parallelism and function parallelism.

Item Type: Book section
DOI/Identification number: 10.1109/ICSMC.1999.823354
Uncontrolled keywords: genetic algorithms; databases; data mining; augmented reality; virtual machining; logic; genetic mutations; biological cells; encoding
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: 09 Sep 2009 18:16 UTC
Last Modified: 16 Feb 2021 12:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21764 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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