Density and closeness clustering based user moving behavior determination method

Abstract

The invention discloses a density and closeness clustering based user moving behavior determination method and belongs to the field of data businesses. The method comprises: introducing a time window, performing time window division on a moving sequence of a user, and dividing the continuous moving sequence into a plurality of window sequences discrete in time; according to a definition of a moving locus, mining out a plurality of moving loci of the user from the window sequences; according to a definition of a characteristic locus set, performing division on the moving loci, including similar loci in a same characteristic locus set, and removing noise data; and according to a definition of a characteristic locus, selecting corresponding central loci as characteristic loci from the characteristic locus set, and finally determining a moving behavior of the user. The method can be applied to practical mobile communication data, so that the randomness and abnormal points of moving of the user can be eliminated; and the determined moving behavior of the user can serve as an input of a position service based position module and also can serve as an input of a mobile recommendation system, so that personalized services can be provided.

Description

technical field [0001] The invention belongs to the field of data services, and in particular relates to a method for determining user mobile behavior based on density and closeness clustering. Background technique [0002] With the widespread popularization of mobile portable devices, the rapid development of wireless communication technology and global positioning technology, people have been able to obtain a large number of real-time location data of users, and the services based on location information in mobile networks are also more and more popular among domestic and foreign researchers. Pay attention, especially in the aspect of user mobile behavior analysis. Usually, user mobile data includes GPS data set and GSM (Global System for Mobile Communication, Global System for Mobile Communication) data set, wherein the GPS data set records the user's longitude, and the GSM data locates the user's position through the base station. For the analysis of user mobile behavio...

AI Technical Summary

Problems solved by technology

Because the data in the actual communication environment is based on the location data of the base station and does not include the GPS information of the user; in addition, the data of the mobile user is very chaotic, with a huge amount of data, including abnormal data of the random movement of the user and the ping-pong switching of the base station due to signal quality Phenomena data, causing great difficulties in extracting user trajectories;

[0007] (2) Regarding the clustering of user trajectories, the existing inventions have general clustering effects due to the difficulty in determining the similarity between trajectories, and the similarity between trajectories of the clustering results is low

The resulting user representative trajectory is less, and the user's movement trajectory information will be lost, which does not conform to the real user's mobile behavior.

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