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Genetic network programming with parallel processing for association rule mining in large and dense databases

Published: 07 July 2007 Publication History

Abstract

Several methods of extracting association rules have been reported. A new evolutionary computation method named Genetic Network Programming (GNP) has also been developed recently and its efectiveness is shown for small datasets. However, it has not been tested for large datasets, particularly in datasets with a large number of attributes. The aim of this paper is to extract association rules from large and dense datasets using GNP considering a real world database with a huge number of attributes. We propose a new method where a large database is divided into many small datasets, then each GNP deals with one dataset having attributes with appropiate size, which was selected randomly from a large dataset and generated genetically. These GNPs are processed in parallel. We then propose some new genetic operations to improve the number of rules extracted and their quality as well. The proposed method improves remarkably on simulations.
Fig. 1 shows the architecture of the proposed method. We use the CLIENT/SERVER model. CLIENT side carries out preprocessing of large database, assignment of files to each server, rule checking, and genetic operations on files. SERVER side carries out processing of each file using conventional GNP based mining method independently. The features and advantages of the proposed method are the following: Rule extraction is done in parallel. Each file generates its local pool of the rules. Files or datasets are treated as individuals in order to do new genetic operations over them and improve the rule extraction. Extracted rules are stored in a global pool. The rules are verified to avoid redundancy among them and it is assured that only new rules are stored.

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  • (2024)Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and ApplicationsArtificial Intelligence for Neuroscience and Emotional Systems10.1007/978-3-031-61140-7_10(98-107)Online publication date: 31-May-2024

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  1. Genetic network programming with parallel processing for association rule mining in large and dense databases

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958

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    Association for Computing Machinery

    New York, NY, United States

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    Published: 07 July 2007

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    Author Tags

    1. association rules
    2. genetic network programming
    3. parallel processing

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2024)Enhancing Interpretability in Machine Learning: A Focus on Genetic Network Programming, Its Variants, and ApplicationsArtificial Intelligence for Neuroscience and Emotional Systems10.1007/978-3-031-61140-7_10(98-107)Online publication date: 31-May-2024

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