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Method based on track triple feature clustering

A trajectory and clustering technology, applied in the direction of instruments, character and pattern recognition, data processing applications, etc., can solve problems such as low value density and large amount of trajectory data

Inactive Publication Date: 2021-04-20
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the amount of trajectory data is large and the value density is low. If you want to explore its hidden value information, you need to analyze and process the trajectory data

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  • Method based on track triple feature clustering

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Embodiment Construction

[0035] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0036] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a method based on track triple feature clustering, and belongs to the technical field of data mining and visualization. The method comprises the following steps: S1, preprocessing track data; S2, converting the preprocessed track data into a high-dimensional vector by using a Doc2Vec method; S3, carrying out clustering on the obtained vector by utilizing k-means; and S4, carrying out visual display on the obtained clustering result, and analyzing the time, space and attribute characteristics of the track to finally obtain the rule of each type of track. According to the method, the defect that attribute constraints are ignored in track space-time clustering is overcome, track clustering is more rigorous, the result is more refined, and travel features are more obvious.

Description

technical field [0001] The invention belongs to the technical field of data mining and visualization, and relates to a method for clustering based on trajectory triple features. Background technique [0002] In recent years, with the vigorous development of global positioning technology, various locatable devices are widely used in people's daily life, making it easier to obtain people's travel trajectories. By mining the hidden behavior patterns of trajectory data, people's travel can be provided better advice. However, the amount of trajectory data is large and the value density is low. To explore its hidden value information, it is necessary to analyze and process the trajectory data. In data mining, clustering analysis is an important method. By using clustering, objects in the space can be divided and classified according to the similarities and differences of distance, attributes, characteristics, etc., so that the original scattered data can be tidied up, so as to be...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06Q10/06G06Q50/26
Inventor 秦红星郭育宁
Owner CHONGQING UNIV OF POSTS & TELECOMM