Class association rule mining from incomplete database using Genetic Network Programming

Kaoru Shimada, Shingo Mabu, Eiji Morikawa, Kotaro Hirasawa, Takayuki Furuzuki

研究成果: Article査読

4 被引用数 (Scopus)

抄録

A method of class association rule mining from incomplete databases is proposed using Genetic Network Programming (GNP). GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. An incomplete database includes missing data in some tuples, however, the proposed method can extract important rules using these tuples, and users can define the conditions of important rules flexibly. Generally, it is not easy for Aprior-like methods to extract important rules from incomplete database, so we have estimated the performances of the rule extraction and classification of the proposed method using incomplete data set. The results showed that the accuracy of classification of the proposed method is favorable even if some tuples include missing data.

本文言語English
ページ(範囲)795-803+16
ジャーナルIEEJ Transactions on Electronics, Information and Systems
128
5
DOI
出版ステータスPublished - 2008

ASJC Scopus subject areas

  • 電子工学および電気工学

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