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

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルSICE Annual Conference, SICE 2007
ページ2686-2693
ページ数8
DOI
出版ステータスPublished - 2007 12 1
イベントSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
継続期間: 2007 9 172007 9 20

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Conference

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

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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