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A Distributed Mining Method for General Adjoint Patterns Based on Large-Scale Trajectory Data

A trajectory data and distributed technology, applied in data mining, special data processing applications, geographic information databases, etc., can solve problems such as inability to mine and distinguish

Active Publication Date: 2021-08-06
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 simultaneously 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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  • A Distributed Mining Method for General Adjoint Patterns Based on Large-Scale Trajectory Data
  • A Distributed Mining Method for General Adjoint Patterns Based on Large-Scale Trajectory Data
  • A Distributed Mining Method for General Adjoint Patterns Based on Large-Scale Trajectory Data

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Embodiment 1

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

[0137] 1. Create a trajectory data set;

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

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

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

[0141] In this embodiment, after step 1, the data is preprocessed first, and then step 2 is performed.

[0142] In this embodiment, the data preprocessing includes: renumbering the original numbers of the moving objects so that the numbers are continuous an...

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Abstract

The present invention relates to the technical field of trajectory data processing, and in particular, relates to a distributed mining method based on large-scale trajectory data, which includes the following steps: 1. Establish a trajectory data set; 2. Distribute the trajectory data set Clustering: Density clustering through DBSCANCD algorithm; 3. TCB algorithm takes the density clustering result as input, and divides the boundary points reasonably by calculating the similarity between members of the set; 4. Distributed mining of trajectory data sets: The GSPR algorithm splits and repartitions the input of general adjoint pattern mining, and then mines through the SAE algorithm. The present invention can better mine common adjoint patterns.

Description

technical field [0001] The invention relates to the technical field of trajectory data processing, in particular to a distributed mining method for general adjoint patterns based on large-scale trajectory data. Background technique [0002] With the popularization and use of mobile devices with positioning functions, the trajectory data has shown an explosive growth. The trajectory data is mostly a time-space sequence, and moving objects with positioning devices are constantly generated at a fixed frequency, which contains rich value. It is of great significance to extract common adjoint patterns in large-scale trajectories, which provides many possibilities for upper-layer services. The mining of general accompanying patterns can be used to improve urban traffic conditions. By discovering the general accompanying patterns, it is possible to predict whether traffic congestion will occur on a certain road in a certain period of time, so as to ease traffic in advance to avoid ...

Claims

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Application Information

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Patent Type & Authority Patents(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