Research On The Application Of The Web Data Mining In The E-commerce

With the development and popularization of the internet, the development of e-commerce—a new type of business mode—has drawn more and more attention from the researchers. People hope to obtain more economic benefits by making full use of the advantages of e-commerce. It has become a hot topic to apply the thought and method of data mining to e-commerce to help the website get real valuable knowledge from a huge amount of information and then give instruction on enterprises' decision and provide more convenient services to the users of e-commerce website. As a brand-new information technology which came forth with the development of data technology in recent years, data-mining combines multi-disciplinary knowledge such as data, artificial intelligence and statistics. Through mining the huge historically accumulated data, it tries to extract unknown, effective and useful knowledge therefrom. On the basis of summarizing the related concepts and functions of data-mining and Web-mining, this paper further studies the major mining ways and specific realizing methods; then it probes into e-commerce and the definition, classification and features of individualized recommendation system as well as the features of the application of data-mining technology; based on the above study, this paper designs an e-commerce platform facing to the Web data-mining technology and constructs an individualized service recommendation system basing on Web using. Through mining the data of Web server log, this system can obtain users' clustering and browsing mode thus can provide individualized services for different kinds of users. Apriori arithmetic of Association Rules and k-means of clustering analyze are brought in when describing the individualized service recommendation system. It studies on the process of realization of data-mining in e-commerce to make the individualized service recommendation come true by solving a series of problems like data pretreatment, data-mining and recommendation mode. Finally come the conclusion and the outlook on the future work.