Abstract: The association rules between transactions are concentrated on meaningful association between different services in data mining.The existing algorithms can realize the largest conversion of frequent itemsets amongand within affairs,and find the relationship between different users.However,indealing with the affairs of the larger database,a large number of false rules are generated due to the correlation analysis is performed on the original database.This paper proposes a mining algorithm of association rules for inter-transaction among affairs based on cluster analysis.Through simplifying the complex dataset by cluster analysis,the proposed approach can eliminate redundant data,reduse the size of dataset and avoid the need for multipole scanning of the database and the generation of a large number of false rules.Experimental results indicate that the proposed method has higher efficiency and the ability of more precisely predicting the interests of users

Key words: Web data mining, inter-transaction association rules, clustering analysis, sliding window