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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.

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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: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 29 Jul 2009 17:52 UTC
Last Modified: 22 Aug 2019 11:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21483 (The current URI for this page, for reference purposes)
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