Class association rule mining for large and dense databases with parallel processing of genetic network programming

Eloy Gonzales, Karla Taboada, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Among several methods of extracting association rules that have been reported, a new evolutionary computation method named Genetic Network Programming (GNP) has also shown its effectiveness for small datasets that have a relatively small number of attributes. The aim of this paper is to propose a new method to extract association rules from large and dense datasets with a huge amount of attributes using GNP. It consists of two-level of processing. Server Level where conventional GNP based mining method runs in parallel and Client Level where files are considered as individuals and genetic operations are carried out over them. The algorithm starts dividing the large dataset into small datasets with appropiate size, and then each of them are dealt with GNP in parallel processing. The new association rules obtained in each generation are stored in a general global pool. We compared several genetic operators applied to the individuals in the Global Level. The proposed method showed remarkable improvements on simulations.

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages4615-4622
Number of pages8
DOIs
Publication statusPublished - 2007 Dec 1
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
Duration: 2007 Sep 252007 Sep 28

Publication series

Name2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
CountrySingapore
Period07/9/2507/9/28

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

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    Gonzales, E., Taboada, K., Shimada, K., Mabu, S., Hirasawa, K., & Hu, J. (2007). Class association rule mining for large and dense databases with parallel processing of genetic network programming. In 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 4615-4622). [4425077] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4425077