Distributed multi-relational data mining based on genetic algorithm

Wenxiang Dou*, Jinglu Hu, Kotaro Hirasawa, Gengfeng Wu

*この研究の対応する著者

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

An efficient algorithm for mining important association rule from multi-relational database using distributed mining ideas. Most existing data mining approaches look for rules in a single data table. However, most databases are multi-relational. In this paper, we present a novel distributed data-mining method to mine important rules in multiple tables (relations) and combine the method with genetic algorithm to enhance the mining efficiency. Genetic algorithm is in charge of finding antecedent rules and aggregate of transaction set that produces the corresponding rule from the chief attributes. Apriori and statistic method is in charge of mining consequent rules from the rest relational attributes of other tables according to the corresponding transaction set producing the antecedent rule in a distributed way. Our method has several advantages over most exiting data mining approaches. First, it can process multi-relational database efficiently. Second, rules produced have finer pattern. Finally, we adopt a new concept of extended association rules that contain more import and underlying information.

本文言語English
ホスト出版物のタイトル2008 IEEE Congress on Evolutionary Computation, CEC 2008
ページ744-750
ページ数7
DOI
出版ステータスPublished - 2008
イベント2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong, China
継続期間: 2008 6月 12008 6月 6

出版物シリーズ

名前2008 IEEE Congress on Evolutionary Computation, CEC 2008

Conference

Conference2008 IEEE Congress on Evolutionary Computation, CEC 2008
国/地域China
CityHong Kong
Period08/6/108/6/6

ASJC Scopus subject areas

  • 計算理論と計算数学
  • 理論的コンピュータサイエンス

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