Location Tracking and Prediction Method for Social Network Users based on Data Mining 

Jian Wang^1,2

1Shandong University, Jinan, 276700, China

2Qilu Institute of Technology, Jinan, 250200, China

Abstract: In order to improve the ability of social network user behavior analysis and trajectory tracking pre-diction, optimize social network construction, based on data mining and behavior analysis, the user characte-ristics of social network are analyzed, and the regularity of social network users is found. A prediction method of social network user location tracking is proposed based on data mining. An association topology model of social network user location distribution is constructed, and segmental feature extraction method is used to extract the relevant feature of social network user location. The Parallel Sets argument axis sorting method is used to schedule social network users' locus storage structure, the implicit pattern of data set is found by fuzzy partition clustering method, and the fuzzy C-means clustering method is used to mine the data. The prediction of social network user location tracking is realized. The simulation results show that the proposed method has high accuracy and high precision of data mining.

Keywords: Data mining; Social network; User location tracking prediction; Feature extraction; Topological model