Research Of E-commerce Based On Association Rules Of Web Mining

Association rule mining is an import part of data mining.Frequent itemset mining is a very important phase of discovering association rules.To some degree,it decides the efficiency of association rule mining algorithm.I study the famous algorithm Apriori and FP-growth,Their advantages and weakness are pointed out.And giving the impoved algorithm named G_apriori based on apriori.The main contents of this paper are as bellow:First,This paper sums up the concepts of data mining and web mining.It Introduces the definition and structure and classification and characteritic of web mining,introducing the procedure and technology and personalized recommendation of web mining in E-commerce.and giving the concept and classification and process of association rules mining.Second,I analyze the famous algorithm apriori and FP-growth.Studying their mentality and description.And demonstrating the two algorithms.And comparing the two algorithms.In this paper,I give an improved algorithm named G_apriori based on Apriori. giving the thought and description of G_apriori.the Apriori algorithm need scan the database more times in the whole procession,So its efficiency is very low.But my improved algorithm need scan database only once.Third,I design a recommendation model of E-commerce based on web mining,I providing the detailed model structure,then introducing the thought and functions of offline model and online model.At last i demonstrate that G_apriori algorithm can get better solution to Personalized recommendation.