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 necessary semantic embedding which would make fully automatic algorithmic analysis possible. Clustering algorithm is an important task in data mining. Clustering algorithms for these moving objects provide new and helpful information, such as Jam detection and significant Location identification.