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General adjoint mode distributed mining method based on large-scale trajectory data

A trajectory data, large-scale technology, applied in data mining, special data processing applications, structured data retrieval, etc., can solve problems such as inability to mine and distinguish

Active Publication Date: 2021-01-26
GUILIN UNIV OF ELECTRONIC TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Existing methods cannot mine and distinguish them, and mining adjoint patterns with loosely connected phenomena requires scanning the entire trajectory, which challenges the performance of adjoint pattern mining algorithms

Method used

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  • General adjoint mode distributed mining method based on large-scale trajectory data
  • General adjoint mode distributed mining method based on large-scale trajectory data
  • General adjoint mode distributed mining method based on large-scale trajectory data

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Experimental program
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Embodiment 1

[0136]Such asFigure 4 As shown, this embodiment provides a general adjoint pattern distributed mining method based on large-scale trajectory data, which includes the following steps:

[0137]1. Establish trajectory data set;

[0138]2. Distributed clustering of the trajectory data set: first perform density clustering through the DBSCANCD algorithm;

[0139]3. The TCB algorithm takes the result of density clustering as input, and divides the boundary points reasonably by calculating the similarity between set members;

[0140]4. Distributed mining of trajectory data sets: GSPR algorithm divides and repartitions the input of general adjoint pattern mining, and then conducts mining through SAE algorithm.

[0141]In this embodiment, after step one, the data is preprocessed first, and then step two is performed.

[0142]In this embodiment, the data preprocessing includes: renumbering the original numbers of the moving objects, making the numbers continuous and starting from 1, while using a fixed frequen...

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Abstract

The invention relates to the technical field of trajectory data processing, in particular to a general adjoint mode distributed mining method based on large-scale trajectory data, which comprises thefollowing steps of: 1, establishing a trajectory data set; 2, performing distributed clustering on the trajectory data set: performing density clustering through a DBSCANCD algorithm; 3, enabling theTCB algorithm to take a density clustering result as input, and boundary points are reasonably divided by calculating the similarity among set members; and 4, performing distributed mining on the trajectory data set: performing segmentation and re-division on input of general adjoint mode mining by a GSPR algorithm, and then performing mining by an SAE algorithm. According to the method, the universal accompanying mode can be better mined.

Description

Technical field[0001]The present invention relates to the technical field of trajectory data processing, and in particular, to a general adjoint pattern distributed mining method based on large-scale trajectory data.Background technique[0002]With the widespread use of mobile devices with positioning functions, the trajectory data has exploded. Most of the trajectory data are time-space sequences. Moving objects with positioning devices are constantly generated at a fixed frequency, which contains rich value. Extracting general adjoint patterns from large-scale trajectories is of great significance and provides many possibilities for upper-layer services. General adjoint pattern mining can be used to improve urban traffic conditions. By discovering general adjoint patterns, it can predict whether traffic congestion will occur on a certain road in a certain period of time, so as to divert traffic in advance to avoid traffic congestion; a group of groups in the same general accompanyin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/29G06K9/62G06F16/2458
CPCG06F16/29G06F16/2465G06F2216/03G06F18/2321G06F18/22
Inventor 张敬伟刘绍建成静张康威杨青
Owner GUILIN UNIV OF ELECTRONIC TECH