The Application Research Of Association Rule Mining In The Website Of Course

Due to the country to promote the construction of quality courses,the fine quality courses network teaching platform,as one of the core content of the course has also been popular.But now the network teaching platform of quality courses is widespread poor interactive,weak personalized learning recommendation function etc., so users using the network teaching platform for learning experience is not good, it has also become a cause that the network teaching platform utilization is not high.Based on the association rules mining technology and AJAX technologies, in software engineering technology as the guide, this paper designed and implemented a quality courses network teaching platform, which had a personalized learning content recommendation functionality and strong interactive features. Personalized learning content recommendation features include two core sub-modules:one is the realization of association rule mining sub-module, the another is the use of association rules to achieve the learning content recommendation sub-modules. In the association rule mining module uses the Apriori algorithm to mining the user’s access logs to get the association rule, to achieve recommending learning content when users access the system; user access based on a hierarchy of content data, this paper also studied the use of ML-SH mining algorithms on the same layer data mining association rules, in order to achieve the effect of access between the plates is recommended.On the basis of platform, the platform of the association rules module has been tested, and the testing process of association rules module possible problems were analyzed. Meanwhile, in order to know users’level of satisfaction for the recommended learning content, the system recommended effect, this paper proposes the use of statistical user accesses the system recommended by the number of its content when the content of a number of visits to the ratio of user satisfaction as a judge way, and in this way testing the effect of the recommendation system and analysis the results and experimental results show that users of the recommended content is satisfied.