Flexible rule mining for difference rules and exception rules from incomplete database

Two flexible rule mining methods from incomplete database are proposed using Genetic Network Programing (GNP). GNP is one of the evolutionary optimization techniques, which uses the directed graph structure. One of the methods extracts the rules showing the different characteristics between different classes in a database. The method can obtain the rules like 'if P then Q' is interesting only in the focusing class. The other one mines interesting rules like even if itemset X and Y have weak or no statistical relation to class item C, the join of X and Y has strong relation to class item C. An incomplete database includes missing data in some tuples. Generally, it is not easy for Apriori-like methods to extract difference rules and exception rules from incomplete database. We have estimated the performances of the rule extraction using incomplete data in the environmental and medical field. © 2010 The Institute of Electrical Engineers of Japan.