Application Of Improved Association Rules In Personalization Website Construction

A Website is composed of several Web pages, on which the information distributes. Different users get interested in different Web pages. However, the unreasonable structure of pages and inefficient link pages decrease the efficiency of accessing pages. Personalization service seems imperative for us to learn about the user's interests and analyze the browsing patterns by means of Web mining technology, so as to automatically recommend a real-time page for the user and meet the need of conglomeration.In the thesis, method and algorithm of information extraction based on dynamic Web pages are analyzed FDLG mines information and produces the association rules using the mining algorithm for frequent patterns. With the help of fuzzy dynamic clustering algorithm, a systematic structure is formed with personalized recommendation.The thesis includes the following parts:(1) Summarize the research result concerning the data mining and discuss the current research situation of the data mining based on association rules.(2) Explore the algorithm for data acquisition in dynamic pages to dramatically decrease the amount of record and improve the recognition rate.(3) Make a study on the association rules-based mining theory, discussing the shortage in the traditional mining algorithm, then providing FDLG based on the fast frequent mining pattern of logic and operation to reduce the amount of calculation and improve the efficiency.(4) Combining the association rules of page access and the results of clustering analysis on access mode, a method of personalized recommendation is provided. It is convenient for personalized recommendation to form similar users and page group by means of clustering the similar users and pages through fuzzy clustering.