Extensible quick trajectory clustering method

Abstract

The invention provides an extensible quick trajectory clustering method. Firstly, calculating local MST (minimum spanning tree); secondly, generating a global MST; thirdly extracting a cluster by the method of coarse-grained parallelism or fine-grained parallelism. [Proposed is a new trajectory clustering method based on point data cluster, time expense is lesser than the traditional cluster algorithm based on the model, distance or density, meanwhile, with the stop proposed by the extensible quick trajectory clustering method, the extensibility of trajectory big data clustering is realized.

Description

technical field [0001] The invention relates to an expandable and fast trajectory clustering method, which belongs to the fields of data mining technology and high-performance computing. Background technique [0002] Currently, there are many successful trajectory clustering methods. Trajectory clustering methods can be broadly classified into three categories, namely model-based, distance-based, and density-based. Model-based approaches model part or all of a trajectory dataset and find a set of fitting parameters for the model that represent different clusters. The models for the entire trajectory clustering are regression mixed models and Markov models, and the EM algorithm is used to estimate the parameters of these models. In these methods, the trajectory is modeled as a whole and thus cannot detect similar parts of the trajectory. [0003] Distance-based clustering algorithms use different trajectory distance functions and general clustering algorithms to cluster tr...

AI Technical Summary

Problems solved by technology

In these methods, the trajectory model is taken as a whole, so similar parts of the trajectory cannot be detected

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