Analysis And Research Of User Data Of Mobile Internet

In today’s society, with the rapid development and popularization of smart mobile devices, the mobile Internet industry is gradually catching up with the traditional Internet industry. The mobile Internet has become an integral part in everyone’s live, work, and study. Through it people can get information, communicate with each other, gain knowledge, share and express anywhere. Correspondingly, the mobile data which carries unlimited valuable information is growing at an exponential rate of speed. But compared with the traditional Internet, mobile Internet has the feature of mobility, complexity, etc., so that everyone in the study of mobile Internet data cannot just copy the method of researching the traditional Internet. How to get full use of existing communication technologies, information collection technology, and data mining technology into the mobile Internet, will be the focus directions in technology applications in the next years.This paper will combine with the characteristics of the mobile Internet, based on the analysis and study of traditional data mining algorithms, and puts forward a hybrid clustering algorithm which integrates the clustering algorithm based on partition and the clustering algorithm based on hierarchical. The algorithm can avoid the problem of randomly chosen initial, cluster centers, and uses the clustering algorithm based on partition to initialize the data, then uses the clustering algorithm based on hierarchical to analysis the post-processed data from the bottom to the top, which can greatly enhance clustering speed. R language tools are used to simulate the algorithm to confirm the rationality and effectiveness of the algorithm. This paper regards the data mining algorithm above, as the theoretical basis, finally designs and implements a mobile Internet data analysis platform. This platform, which consists of a data acquisition module, a data analysis module, a data storage module, a data display module, a user management module and other components, applies the data mining algorithm designed by this paper to analyze the mobile Internet data, and presents the data after analysis to users, eventually confirms the feasibility of the algorithm once again.