The Design And Enforcement Of Data Mining System Based On Network User Behavior

With the development of computer science and technology in the 21 st century, the internet has been widely spreaded. Those network service providers, who mainly dealing in the information and communication industry, are gradually enhancing to leisure time entertainment, electronic service, e-commerce three categories of service industry, nowadays the internet user’s behavior becomes more and more complicated, accompanied with accumulated massive network user data. All kinds of commercial websites and public websites need to utilize these user data well to dig out the valuable analysis. On the other hand, commercial websites also need to analyze user behavior and preferences in pursuit of profit. Therefore, understanding the Internet, data analysis of network user behavior becomes more and more important.The main contents of this paper to set up a network user behavior concept and classification, and assort different users; analyze the information source of network behavior analysis system, and find out the hidden rules and patterns of the network user behavior; discuss the requirements, procedures and range to establish this system, as well as some key technologies in the process of the design and realization the system, then restate several commonly used analysis system model or theory. By comparing the clustering analysis, association rule algorithm and other related mining algorithm, the Apriori algorithm is selected as the user behavior data mining system’s core algorithm, and the algorithm is further improved and optimized.This paper presents a data mining model based on some own characteristics and user behavior in the network data mining algorithm, and the system is consisted of 3 main function modules, including data acquisition module, data processing module, and interest mining module, and dig out the demanded data through t the core Apriori algorithm, and proceeds related experimental research, by analyzing the test results, then summarized the related rules of network user behavior and their social network..