DenTrac: A Density based Trajectory Clustering Tool

A Trajectory Data Clustering Method Based On Dynamic Grid Density

Under the traditional method of frequent trajectory mining, the location of data is obtained through the GPS device. However, limited equipment accuracy may incur location ambiguity. In this paper, we propose a new trajectory data clustering method based on dynamic grid density, in order to remove this ambiguity. In this method, the trajectory space of an object is firstly divided into equal-si...

Self-adaptive Trajectory Prediction Method Based on Density Clustering

To solve the problem of user trajectory prediction in the mobile communication environment, we proposed a self-adaptive trajectory prediction method (ATPDC) based on density clustering. And it consists of two stages which are trajectory modeling stage and trajectory updating stage respectively. In the first stage, it constructs the user trajectory prediction model by clustering historical traje...

Density Based k-Nearest Neighbors Clustering Algorithm for Trajectory Data

With widespread availability of low cost GPS, cellular phones, satellite imagery, robotics, Web traffic monitoring devices, it is becoming possible to record and store data about the movement of people and objects at a large amount. While these data hide important knowledge for the enhancement of location and mobility oriented infrastructures and services, by themselves, they demand the necessa...

Shape-based Trajectory Clustering

Automatic trajectory classification has countless applications, ranging from the natural sciences, such as zoology and meteorology, to urban planning and sports analysis, and has generated great interest and investigation. The purpose of this work is to propose and test new methods for trajectory clustering, based on shape, rather than spatial position, as is the case with previous methods. The...

Sampling-based Sub-Trajectory Clustering