TAD: A trajectory clustering algorithm based on spatial-temporal density analysis

作者：

Highlights：

• A function NMAST is defined to effectively measure the density of trajectory data.

• A noise tolerance factor is given to measure the influence of noise.

• A clustering algorithm TAD is proposed for spatial-temporal trajectory data.

• TAD is applied on LAMOST skylight spectra to reveal the distribution features.

摘要

•A function NMAST is defined to effectively measure the density of trajectory data.•A noise tolerance factor is given to measure the influence of noise.•A clustering algorithm TAD is proposed for spatial-temporal trajectory data.•TAD is applied on LAMOST skylight spectra to reveal the distribution features.

论文关键词：Trajectory clustering,Density function,Neighbourhood move ability,Noise tolerance factor,Sky background,LAMOST

论文评审过程：Received 17 December 2018, Revised 25 July 2019, Accepted 26 July 2019, Available online 2 August 2019, Version of Record 7 August 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.112846