Research on Model and Algorithm of User Access Pattern Data Mining

Abstract

The era of big data has come. Today, all kinds of information and data are showing explosive growth. Internet activities of different scales are struggling to catch up with the pace and pace of the development of "big data. This paper uses time series mining algorithms, and uses Microsoft's data mining tools to model the data sets collected from data halls, so as to discover the user's online behaviour patterns and potential online rules within a certain period of time. we made reasonable suggestions for the scientific management of the campus network. A new clustering method based on the improved Kohonen self-organizing feature mapping neural network is proposed. A Gaussian-shaped membership function has introduced to output several neurons with a degree of membership greater than the threshold, thereby solving the problem of mining users' multiple interests.