Handset sensor-based group behavior analysis method

Abstract

The present invention relates to a handset sensor-based group behavior analysis method, and belongs to the technical field of computer network applications. According to the technical scheme of the invention, a handset acceleration signal and a touch screen sensor signal are adopted as a data source for behavior analysis. After that, original data are subjected to noise treatment through a sliding average filter. The features of pre-processed data are extracted in the time domain and in the frequency domain. In this way, a dimension reduction method combined with the mutual information and the linear discriminant analysis is constructed for the dimension reduction treatment of extracted features. After the dimension reduction treatment, corresponding feature vectors are input into a behavior classification model, and then a user behavior recognition result is obtained. Furthermore, the access time and the access frequency are selected as group behavior attributes. Finally, based on the affinity propagation algorithm, the data of user group behaviors are clustered, so that the group behavior analysis is completed.

Description

technical field [0001] The invention relates to the technical field of behavior recognition and knowledge mining, in particular to a group behavior method based on mobile phone sensors. Background technique [0002] With the rapid development and wide application of mobile phone networks, the number of mobile phone network users continues to increase, and mobile networks have become the most important media for people to share resources and exchange information. The prosperity of mobile phone networks has led to explosive growth of user data, and these massive data contain huge value. The analysis of network group behavior can not only provide users with highly adaptable personalized information services, but also maintain cyberspace security in a wider range, which has certain research value. Network group behavior is a kind of behavior habit shown by most or all network users in network activities. In network activities, all network users are regarded as a whole, and the...

AI Technical Summary

Problems solved by technology

The prior art user group behavior analysis method mainly analyzes network group behavior by obtaining smart terminal logs. However, obtaining smart terminal logs requires higher authority, and at the same time, when processing massive data of Web logs, existing search engines cannot When technology retrieves information, there are still problems such as low search accuracy and limited coverage, and it is impossible to better discover potential and hidden knowledge on Web logs.

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