Class association rule mining from incomplete database using Genetic Network Programming

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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)795-803+16
JournalIEEJ Transactions on Electronics, Information and Systems
Volume128
Issue number5
DOIs
Publication statusPublished - 2008

Keywords

  • Association rules
  • Data mining
  • Genetic Network Programming
  • Missing data

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Class association rule mining from incomplete database using Genetic Network Programming'. Together they form a unique fingerprint.

  • Cite this