Class Association Rule Mining from Incomplete Database Using Genetic Network Programming

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  • Shimada Kaoru
    Information, Production and Systems Research Center, Waseda University
  • Mabu Shingo
    Graduate School of Information, Production and Systems, Waseda University
  • Morikawa Eiji
    Graduate School of Information, Production and Systems, Waseda University
  • Hirasawa Kotaro
    Graduate School of Information, Production and Systems, Waseda University
  • Furuzuki Takayuki
    Graduate School of Information, Production and Systems, Waseda University

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  • 遺伝的ネットワークプログラミングによる不完全データベースからのクラス相関ルールの抽出
  • イデンテキ ネットワーク プログラミング ニ ヨル フカンゼン データベース カラノ クラス ソウカン ルール ノ チュウシュツ

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Abstract

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.

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