Time-focused clustering of trajectories of moving objects

350Citations
Citations of this article
266Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Spatio-temporal, geo-referenced datasets are growing rapidly, and will be more in the near future, due to both technological and social/commercial reasons. From the data mining viewpoint, spatio-temporal trajectory data introduce new dimensions and, correspondingly, novel issues in performing the analysis tasks. In this paper, we consider the clustering problem applied to the trajectory data domain. In particular, we propose an adaptation of a density-based clustering algorithm to trajectory data based on a simple notion of distance between trajectories. Then, a set of experiments on synthesized data is performed in order to test the algorithm and to compare it with other standard clustering approaches. Finally, a new approach to the trajectory clustering problem, called temporal focussing, is sketched, having the aim of exploiting the intrinsic semantics of the temporal dimension to improve the quality of trajectory clustering. © Springer Science + Business Media, LLC 2006.

Cite

CITATION STYLE

APA

Nanni, M., & Pedreschi, D. (2006). Time-focused clustering of trajectories of moving objects. In Journal of Intelligent Information Systems (Vol. 27, pp. 267–289). https://doi.org/10.1007/s10844-006-9953-7

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free