Genetic network programming with class association rule acquisition mechanisms from incomplete database

Abstract

A method of class association rule mining from incomplete databases is proposed using Genetic Network Programming (GNP). An incomplete database includes missing data in some tuples, however, the proposed method can extract important rules using these tuples. The proposed mechanisms can calculate measurements of association rules directly using GNP. GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. Users can define the conditions of important rules flexibly and obtain enough number of important rules. Generally, it is not easy for Aprior-like methods to extract important rules from incomplete database. 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.