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

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

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.

元の言語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
出版物ステータス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
Singapore
期間07/9/2507/9/28

    フィンガープリント

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Theoretical Computer Science

これを引用

Gonzales, E., Taboada, K., Shimada, K., Mabu, S., Hirasawa, K., & Hu, J. (2007). Class association rule mining for large and dense databases with parallel processing of genetic network programming. : 2007 IEEE Congress on Evolutionary Computation, CEC 2007 (pp. 4615-4622). [4425077] (2007 IEEE Congress on Evolutionary Computation, CEC 2007). https://doi.org/10.1109/CEC.2007.4425077