Association rule discovery is a method that detects associative relationships between items or attributes in transactions. It is one of the most widely studied problems in data mining because it offers useful insight into the types of dependencies that exist in a data set. However, most studies on association rule discovery have the drawback that they can not discover association rules among user groups that have common characteristics. To solve this problem, we segment the set of users into user-subgroups by using feature selection and the user segmentation, thus local association rules in user-subgroup can be discovered. To evaluate that the local association rules are more appropriated than the global association rules in each user-subgroup, derived local association rules are compared with global association rules in terms of several evaluation measures.