Hierarchical association rule mining in large and dense databases using genetic network programming

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

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

Abstract

In this paper we propose a new hierarchical method to extract association rules from large and dense datasets using Genetic Network Programming (GNP) considering a real world database with a huge number of attributes. It uses three ideas. First, the large database is divided into many small datasets. Second, these small datasets are independently processed by the conventional GNP-based mining method (CGNP) in parallel. This level of processing is called Local Level. Finally, new genetic operations are carried out for small datasets considered as individuals in order to improve the number of rules extracted and their quality as well. This level of processing is called Global Level. The amount of small datasets is also important especially for avoiding the overload and improving the general performance; we find the minimum amount of files needed to extract important association rules. The proposed method shows its effectiveness in simulations using a real world large and dense database.

Original languageEnglish
Title of host publicationSICE Annual Conference, SICE 2007
Pages2686-2693
Number of pages8
DOIs
Publication statusPublished - 2007 Dec 1
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
Duration: 2007 Sep 172007 Sep 20

Publication series

NameProceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CountryJapan
CityTakamatsu
Period07/9/1707/9/20

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Keywords

  • Association rules
  • Data mining
  • Genetic network programming
  • Parallel processing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Gonzales, E., Shimada, K., Mabu, S., Hirasawa, K., & Hu, J. (2007). Hierarchical association rule mining in large and dense databases using genetic network programming. In SICE Annual Conference, SICE 2007 (pp. 2686-2693). [4421446] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2007.4421446