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

In this paper, a novel trajectory clustering algorithm - TAD - is proposed to extract trajectory Stays based on spatial-temporal density analysis of data. Two new metrics - NMAST (Neighbourhood Move Ability and Stay Time) density function and NT (Noise Tolerance) factor - are defined in this algorithm. Firstly, NMAST integrates the characteristics of Neighbourhood Move Ability (NMA, extended from the concept of Move Ability MA), Stay Time (ST), and evaluation factor EA to measure the spatial-temporal density of data. Secondly, NT utilizes the features of noise to dynamically evaluate and reduce the influence of noise. The experimental results on Geolife dataset shows that the distributions hidden in data are extracted more realistically, especially for various complex or special trajectories with long-duration gaps. Furthermore, our analytical method of trajectory data is particularly applied in the spectra of LAMOST survey to analyse the variation characteristics of sky-background. The results show a regular distribution on observational date which is relatively concentrated in the month of 1, 10, 11, 12 in each year. The laws discovered in this work would provide a reasonable support for the designation of observational plans, and the new trajectory analysis method would also provide the services for the astronomical data analysis and then for the further studies of formation and evolution of the universe. (C) 2019 Elsevier Ltd. All rights reserved.