A method of association rule analysis for incomplete database using genetic network programming

Abstract

A method of 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. Previous rule mining approaches cannot handle incomplete data directly. The proposed method can extract rules directly from incomplete data without generating frequent itemsets used in conventional approaches. In this paper, the proposed method is combined with difference rule mining using GNP for flexible association analysis. We have evaluated the performances of the rule extraction from incomplete medical datasets generated by random missing values. In addition, artificial missing values for privacy hiding are considered using the proposed method.