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

Eloy Gonzales, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Takayuki Furuzuki

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 publicationProceedings of the SICE Annual Conference
Pages2686-2693
Number of pages8
DOIs
Publication statusPublished - 2007
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu
Duration: 2007 Sep 172007 Sep 20

Other

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

Fingerprint

Association rules
Processing

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

Cite this

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

Hierarchical association rule mining in large and dense databases using genetic network programming. / Gonzales, Eloy; Shimada, Kaoru; Mabu, Shingo; Hirasawa, Kotaro; Furuzuki, Takayuki.

Proceedings of the SICE Annual Conference. 2007. p. 2686-2693 4421446.

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

Gonzales, E, Shimada, K, Mabu, S, Hirasawa, K & Furuzuki, T 2007, Hierarchical association rule mining in large and dense databases using genetic network programming. in Proceedings of the SICE Annual Conference., 4421446, pp. 2686-2693, SICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007, Takamatsu, 07/9/17. https://doi.org/10.1109/SICE.2007.4421446
Gonzales E, Shimada K, Mabu S, Hirasawa K, Furuzuki T. Hierarchical association rule mining in large and dense databases using genetic network programming. In Proceedings of the SICE Annual Conference. 2007. p. 2686-2693. 4421446 https://doi.org/10.1109/SICE.2007.4421446
Gonzales, Eloy ; Shimada, Kaoru ; Mabu, Shingo ; Hirasawa, Kotaro ; Furuzuki, Takayuki. / Hierarchical association rule mining in large and dense databases using genetic network programming. Proceedings of the SICE Annual Conference. 2007. pp. 2686-2693
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