A Review of the Mobile Trajectory Clustering Methods

doi:10.13474/j.cnki.11-2246.2018.0001

Abstract

Abstract:

Mobile trajectory data is the representation of human mobile behavior.Mining this kind of data and discovering their latent semantic information can map the people's travel patterns and social attribute,etc.The clustering method based on similarity is a common method of data mining.Trajectory clustering can be used to find the object group of the similar behavior,so as to find the relevant moving patterns.According to the characteristics of the trajectory data,the trajectory data model is divided into trajectory point model and trajectory segment model,and the corresponding similarity measure is defined:spatial similarity measure and spatio-temporal similarity measure.Then,the clustering methods of two models are reviewed,and the advantages and disadvantages of the algorithms are also summarized,which provide the basis for effective selection of clustering algorithms.Finally,the development trend of the research on the clustering method of the moving trace data is discussed.

Key words: mobile trajectory data, data mining, the clustering method, research summary

CLC Number:

P208

MOU Naixia, XU Yujing, ZHANG Hengcai, CHEN Jie, ZHANG Lingxian, LIU Xiliang. A Review of the Mobile Trajectory Clustering Methods[J]. 测绘通报, 2018, 0(1): 1-7.

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