Using a Data Mining Tool and FP-growth Algorithm Application for Extraction of the Rules in Two Different Dataset

In this paper, we want to improve association rules in order to be used in recommenders. Recommender systems present a method to create personalized offers. One of the most important types of recommender systems is the collaborative filtering that deals with data mining in user information and offering them the appropriate item. Among the data mining methods, finding frequent item sets and creating association rules are included in dataset. In this method, through separating the data of more active users, those who are interested in more items, we make sample from the training set and continue finding the association rules on the selected sample. Therefore, while the training set gets smaller, the production speed of rules increases. At the same time, we will show that the quality of the produced rules has been improved. Among the advantages of the proposed method, it can be referred to its simplicity and rapid implementation. Moreover, through a sampling from training set, the speed of association rules will be increased.