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Track clustering method based on semantic similarity

A technology of semantic similarity and trajectory clustering, which is applied in the direction of instruments, character and pattern recognition, integrated learning, etc., can solve the problems of unreasonable clustering results and low efficiency, reduce clustering uncertainty, improve efficiency, The effect of reducing the number of calculations

Active Publication Date: 2021-03-09
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0003] In view of the above research problems, the purpose of the present invention is to provide a trajectory clustering method based on semantic similarity, which solves the problem of low efficiency and unreasonable clustering results when the similarity measure in the prior art is used to mine data. The problem

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  • Track clustering method based on semantic similarity
  • Track clustering method based on semantic similarity

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Embodiment Construction

[0042] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0043]A trajectory clustering method based on semantic similarity, the following steps:

[0044] S1. Define the semantic trajectory based on the application field of the data to be mined, and then obtain the semantic trajectory similarity based on the defined semantic trajectory; wherein, the application field of the data to be mined is the social network field including latitude and longitude, scene label, time and weather information , transportation or tourism, and other fields that contain relevant data information.

[0045] The steps of semantic trajectory similarity are as follows:

[0046] S1.1. Given a semantic trajectory sequence T i ={t i，1 , t i，2 ,...,t i，j ,...t i，n}, where n is the number of points on the trajectory, t i，j is the trajectory T i The jth point of t i，j by m attributes (p 1 ,p 2 ,...,p m ), each of the m ...

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Abstract

The invention discloses a track clustering method based on semantic similarity, belongs to the technical field of clustering methods, and solves the problems of relatively low efficiency and unreasonable clustering result when similarity measurement is used for mining data in the prior art. Semantic trajectories are defined based on the application field of data needing to be mined, and then semantic trajectory similarity is obtained based on the semantic trajectories; giving a trajectory training data set, extracting a plurality of trajectories, and calculating a similarity threshold by adopting a box type graph based on the defined semantic trajectory similarity; and clustering the tracks in the track set based on the similarity threshold. The method is used for trajectory clustering.

Description

technical field [0001] A trajectory clustering method based on semantic similarity is used for trajectory clustering and belongs to the technical field of clustering methods. Background technique [0002] Similarity measurement is an important research problem in trajectory data analysis. For most trajectory data mining problems, comparison between trajectories is required. Therefore, the complexity of trajectory similarity measurement will directly affect the operation of related technologies. efficiency and feasibility. In the prior art, the similarity measurement is mostly implemented by dynamic programming, which needs to calculate the pairwise distance of all trajectory points, specifically: dynamic programming needs to calculate the distance from each point of each trajectory to all points of all other trajectories, and the time is complicated The degree is very high, it is 0(n 2 ), n is the number of points, when the number of trajectories increases a lot, the requi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N20/20
CPCG06N20/20G06F18/23G06F18/22G06F18/214
Inventor 牛新征刘鹏飞望馨何玲杨胜瀚陈冬子刘鹏鹏王芳姝
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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