Genetic Network Programming Based Class Association Rule Mining with Attributes Importance for Large Attributes Set

In order to extract class association rules more effectively when dealing with large attributes set, Genetic Network Programming (GNP) based class association rule mining with Attributes Importance has been proposed in this paper. The main difference between the proposed method and the conventional GNP-based class association rule mining is that Attributes Importance is introduced to affect the attributes selection and genetic operations during the GNP evolution process. The comparison has been carried out by applying the proposed method and the conventional GNP-based class association rule mining to the rules extraction with regard to the interested products on the Internet shop for different customers. The simulation results shows that the efficiency of rules extraction is improved greatly by adopting the proposed method.