PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Nanni M., Pedreschi D. Time-focused density-based clustering of trajectories of moving objects. In: Journal of Intelligent Information Systems, vol. 27 (3) pp. 267 - 289. Mining Spatio-Temporal Data. Gennady Andrienko, Donato Malerba, Michael May and Maguelonne Teisseire (eds.). Springer Netherlands, 2006.
 
 
Abstract
(English)
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.
Subject Spatio-temporal data mining, Trajectory clustering
I.5.3 Clustering
62-07 Data analysis


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