Time-focused clustering of trajectories of moving objects (Articolo in rivista)

Type
Label
  • Time-focused clustering of trajectories of moving objects (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10844-006-9953-7 (literal)
Alternative label
  • Nanni, Mirco; Pedreschi, Dino (2006)
    Time-focused clustering of trajectories of moving objects
    in Journal of intelligent information systems
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Nanni, Mirco; Pedreschi, Dino (literal)
Pagina inizio
  • 267 (literal)
Pagina fine
  • 289 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.springerlink.com/content/l0277414n8r6h478/ (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 27 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (literal)
Note
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ISTI Institute of CNR Dipartimento di Informatica - Università di Pisa (literal)
Titolo
  • Time-focused clustering of trajectories of moving objects (literal)
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. (literal)
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