A Framework for Generating Network-Based Moving Objects
Geoinformatica
Matching Polygonal Curves with Respect to the Fréchet Distance
STACS '01 Proceedings of the 18th Annual Symposium on Theoretical Aspects of Computer Science
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
A Trajectory Clustering Algorithm Based on Symmetric Neighborhood
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 03
Clustering Trajectories of Moving Objects in an Uncertain World
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
DENCLUE 2.0: fast clustering based on kernel density estimation
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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Analyzing trajectories is important and has many applications, such as surveillance, analyzing traffic patterns and hurricane path prediction. In this paper, we propose a unique, non-parametric trajectory density estimation approach to obtain trajectory density functions that are used for two purposes. First, a density-based clustering algorithm DENTRAC that operates on such density functions is introduced. Second, unique post-analysis techniques that use the trajectory density function are proposed. Our method is capable of ranking trajectory clusters based on different characteristics of density clusters, and thus has the ability to summarize clusters from different perspectives, such as the compactness of member trajectories or the probability of their occurrence. We evaluate the proposed methods on synthetic traffic and real-world Atlantic hurricane datasets. The results show that our simple, yet effective approach extracts valuable knowledge from trajectories that is difficult to obtain with other approaches.