User normal track analysis method based on movable position application

A mobile position and trajectory analysis technology, which is applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem that the applicability of clustering algorithm results is not very wide, and achieve the effect of improving applicability

Active Publication Date: 2015-05-13
南京烽火星空通信发展有限公司
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Problems solved by technology

However, most developers have made various specific settings for the input factors of the clustering algorithm base

Method used

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  • User normal track analysis method based on movable position application
  • User normal track analysis method based on movable position application
  • User normal track analysis method based on movable position application

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Experimental program
Comparison scheme
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Embodiment 1

[0052] figure 1 It is an overall flow chart of the present invention, and the specific implementation process is introduced as follows:

[0053] 1> Prepare a batch of existing user trajectory sample data. After cleaning, ensure that the data must contain information such as user account, longitude, latitude, and collection time;

[0054] 2> Extract the original trajectory data of user A from the sample data in step 1 as the input trajectory data. The specific original input trajectory information of user A is as follows: figure 2 shown;

[0055] 3> After obtaining the original trajectory map of user A, use the open source convex hull algorithm ConvexHull to calculate the activity range of user A, such as image 3 As shown, it is a convex pentagon surrounded by A.B.C.D.E coordinate points.

[0056] 4> Calculate the area of ​​user A's activity range, that is, the area of ​​the convex pentagon. A convex pentagon can be cut into three triangles. Among them, the side lengths ...

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Abstract

The invention discloses a user normal track analysis method based on a movable position application. The user normal track analysis method based on the movable position application includes the following steps that firstly, a user track set P is input; secondly, an orderly convex polygonal track set Q comprising all Pn track points is obtained from the track set P; thirdly, the area of the convex polygon Q is calculated; fourthly, the cluster radius R and cluster density T are determined; fifthly, all core clusters are recorded; sixthly, the core clusters are combined; seventhly, recursion is executed until all the core clusters can not be combined, and then it is judged as convergence; eighthly, the points not contained in the core clusters are judged as noise points, the cluster radius R serves as the threshold value, and the noise points are gathered into a plurality of noise clusters through a proximity clustering principle; ninthly, intra-cluster track point election is carried out on the core clusters and the noise clusters, and it is guaranteed that one point is elected from one cluster.

Description

technical field [0001] The invention relates to the field of mobile data track analysis, and is a method for mining and analyzing user activity track data to obtain a user's normal track. Background technique [0002] With the advancement of science and technology, mobile applications have profoundly changed people's daily life. For application developers, in order to provide users with better and more humanized services, it is often necessary to analyze various user behavior data. Analysis and mining, and user activity track data is one of the important ones. [0003] At present, there are many algorithms for mining and analyzing trajectory data, and clustering algorithms are also the first choice. However, most developers have made various specific settings for the input factors of the clustering algorithm based on their own development needs, resulting in the results of the clustering algorithm not having wide applicability. Contents of the invention [0004] Purpose ...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 陈磊李名臣史波良
Owner 南京烽火星空通信发展有限公司
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