Self-adaptive mechanism in genetic network programming for mining association rules

Karla Taboada, Kaoru Shimada, Shingo Mabu, Kotaro Hirasawa, Jinglu Hu

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper we propose a method of association rule mining using Genetic Network Programming (GNP) with a self-adaptation mechanism in order to improve the performance of association rule extraction systems. GNP is a kind of evolutionary methods, whose directed graphs are evolved to find a solution as individuals. Self-adaptation behavior in GNP is related to adjust the setting of control parameters such as crossover and mutation rates. It is called self-adaptive because the algorithm controls the setting of these parameters itself - embedding them into an individual's genome and evolving them. The aim is not only to find suitable adjustments but to do this efficiently. Our method can measure the significance of the association via the chi-squared test and obtain a sufficient number of important association rules. Extracted association rules are stored in a pool all together through generations and reflected in three genetic operators as acquired information. Further, our method can contain negation of attributes in association rules and suit association rule mining from dense databases.

本文言語English
ホスト出版物のタイトル2006 SICE-ICASE International Joint Conference
ページ6007-6012
ページ数6
DOI
出版ステータスPublished - 2006 12 1
イベント2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
継続期間: 2006 10 182006 10 21

出版物シリーズ

名前2006 SICE-ICASE International Joint Conference

Conference

Conference2006 SICE-ICASE International Joint Conference
CountryKorea, Republic of
CityBusan
Period06/10/1806/10/21

ASJC Scopus subject areas

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

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