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

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2007 IEEE Congress on Evolutionary Computation, CEC 2007
ページ4615-4622
ページ数8
DOI
出版ステータスPublished - 2007 12 1
イベント2007 IEEE Congress on Evolutionary Computation, CEC 2007 - , Singapore
継続期間: 2007 9 252007 9 28

出版物シリーズ

名前2007 IEEE Congress on Evolutionary Computation, CEC 2007

Conference

Conference2007 IEEE Congress on Evolutionary Computation, CEC 2007
国/地域Singapore
Period07/9/2507/9/28

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

フィンガープリント

「Class association rule mining for large and dense databases with parallel processing of genetic network programming」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル