Hotspot path analysis method based on density clustering

Abstract

The invention discloses a hotspot path analysis method based on density clustering. The method comprises the following steps: representing a target path as a path point set consisting of a plurality of path points; constructing a similarity distance matrix, comparing the similarity between every two path point sets, calculating clusters formed by the path point sets by adopting a density clustering iterative formula on the basis of the similarity distance matrix, a distance threshold and a density threshold MinPts, and finally outputting the path sets of the clusters as target hotspot paths ina many-to-many manner. The method has the advantages that (1) a similarity comparison method for a target path point set is provided; (2) the selection of the density threshold MinPts has certain flexibility and robustness; and (3) the calculation cost is low, and engineering of the implementation method is realized.

Description

technical field [0001] The invention relates to the field of target path analysis and mining, in particular to a method for analyzing hotspot paths based on density clustering. Background technique [0002] As we all know, the amount of measurement data related to the target path is increasing, and it is difficult to summarize the target path law in a timely and accurate manner only by manual analysis and processing, and it is difficult to support high-real-time auxiliary decision-making in a timely manner. Most of the traditional target path analysis and prediction technologies focus on the target position measurement data, without analysis based on key path points, unable to focus on high-level path features, extract multi-granularity target path patterns, and have high computational costs. Contents of the invention [0003] Purpose of the invention: Aiming at the problems of the prior art, the present invention proposes a method for analyzing hotspot paths based on dens...

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Problems solved by technology

[0002] As we all know, the amount of measurement data related to the target path is increasing, and it is difficult to summarize the law of the target path in a timely and accurate manner only by manual analysis and processing, and it is difficult to support high-real-time auxiliary decision-making in a timely manner.

Most of the traditional target path analysis and prediction technologies are aimed at the measurement data of the target position, without analysis based on key path points, unable to focus on high-level path features and extract multi-granularity target path patterns, and the calculation cost is high

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