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A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction

Freitas, Alex A. (1997) A Genetic Programming Framework for Two Data Mining Tasks: Classification and Generalized Rule Induction. In: Koza, J.R. and Deb, K., eds. Genetic Programming 1997: Proceedings of the Second Annual Conference. Morgan Kaufmann, pp. 96-101. ISBN 978-1-55860-483-4. (KAR id:21483)

Abstract

This paper proposes a genetic programming (GP) framework for two major data mining tasks, namely classification and generalized rule induction. The framework emphasizes the integration between a GP algorithm and relational database systems. In particular, the fitness of individuals is computed by submitting SQL queries to a (parallel) database server. Some advantages of this integration from a data mining viewpoint are scalability, data-privacy control and automatic parallelization.

Item Type: Book section
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
Funders: Stanford University (https://ror.org/00f54p054)
Depositing User: Mark Wheadon
Date Deposited: 29 Jul 2009 17:52 UTC
Last Modified: 12 Jul 2022 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21483 (The current URI for this page, for reference purposes)

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