Distributed multi-relational data mining based on genetic algorithm

Wenxiang Dou, Takayuki Furuzuki, Kotaro Hirasawa, Gengfeng Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2008 IEEE Congress on Evolutionary Computation, CEC 2008
Pages744-750
Number of pages7
DOIs
Publication statusPublished - 2008
Event2008 IEEE Congress on Evolutionary Computation, CEC 2008 - Hong Kong
Duration: 2008 Jun 12008 Jun 6

Other

Other2008 IEEE Congress on Evolutionary Computation, CEC 2008
CityHong Kong
Period08/6/108/6/6

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Dou, W., Furuzuki, T., Hirasawa, K., & Wu, G. (2008). Distributed multi-relational data mining based on genetic algorithm. In 2008 IEEE Congress on Evolutionary Computation, CEC 2008 (pp. 744-750). [4630879] https://doi.org/10.1109/CEC.2008.4630879