Moving object track clustering method based on multi-feature fusion and clustering ensemble

Abstract

The invention discloses a moving object track clustering method based on multi-feature fusion and clustering ensemble. The method comprises the steps of firstly roundly capturing the feature information of the track of a target moving object; then performing clustering analysis on four selected moving track features and generating a plurality of primary clustering results by using a K-means clustering algorithm; quantizing the quality of the plurality of primary clustering results, and then obtaining three fusion clustering result by means of weighted summation; and further integrating the three fusion clustering results to generate a final integration clustering result. According to the method, the feature information of the target moving object can be comprehensively captured, relevance between the dynamic characteristic of the track and time slice can be restored to the utmost extent, and the good antijamming capability is provided; weights are distributed to the plurality of primary clustering results according to different clustering quality assessment criteria, the class number can be automatically recognized during the fusion process, and the intrinsic structure of the class cluster can be effectively captured.

Description

technical field [0001] The invention belongs to the technical field of trajectory data clustering, in particular to a moving object trajectory clustering method based on multi-feature fusion and clustering integration. Background technique [0002] Clustering of moving object trajectories is a very active application research direction of data mining. Its purpose is to classify trajectory data. Similar trajectories are gathered in the same category, while different trajectories are gathered in different categories. Here, the categories Also called a cluster. According to the clustering result of the trajectory of the moving object, the behavior pattern recognition of the moving object can be carried out. For example: clustering the moving trajectories of customers in shopping malls can analyze the flow of people in shopping malls, hot stores, customer shopping behavior patterns, etc.; clustering the moving trajectories of vehicles on main traffic arteries can establish an i...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a mobile object trajectory clustering method based on multi-feature fusion and clustering integration, aiming to solve the problem that traditional clustering algorithms have to deal with the complex dynamic characteristics, high dimensionality and massiveness of trajectory data. Problems that do not yield better results

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