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, Takayuki Furuzuki

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
Event2007 IEEE Congress on Evolutionary Computation, CEC 2007 -
Duration: 2007 Sep 252007 Sep 28

Other

Other2007 IEEE Congress on Evolutionary Computation, CEC 2007
Period07/9/2507/9/28

Fingerprint

Network Programming
Genetic Network
Association Rule Mining
Association rules
Parallel Processing
Genetic Programming
Association Rules
Processing
Evolutionary algorithms
Attribute
Servers
Genetic Operators
Evolutionary Computation
Large Data Sets
Mining
Server
Class
Simulation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

Cite this

Gonzales, E., Taboada, K., Shimada, K., Mabu, S., Hirasawa, K., & Furuzuki, T. (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] https://doi.org/10.1109/CEC.2007.4425077

Class association rule mining for large and dense databases with parallel processing of genetic network programming. / Gonzales, Eloy; Taboada, Karla; Shimada, Kaoru; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki.

2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 4615-4622 4425077.

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

Gonzales, E, Taboada, K, Shimada, K, Mabu, S, Hirasawa, K & Furuzuki, T 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., 4425077, pp. 4615-4622, 2007 IEEE Congress on Evolutionary Computation, CEC 2007, 07/9/25. https://doi.org/10.1109/CEC.2007.4425077
Gonzales E, Taboada K, Shimada K, Mabu S, Hirasawa K, Furuzuki T. 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. 2007. p. 4615-4622. 4425077 https://doi.org/10.1109/CEC.2007.4425077
Gonzales, Eloy ; Taboada, Karla ; Shimada, Kaoru ; Mabu, Shingo ; Hirasawa, Kotaro ; Furuzuki, Takayuki. / Class association rule mining for large and dense databases with parallel processing of genetic network programming. 2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. pp. 4615-4622
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