Visually-driven analysis of movement data by progressive clustering (Articolo in rivista)

Type
Label
  • Visually-driven analysis of movement data by progressive clustering (Articolo in rivista) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1057/palgrave.ivs.9500183 (literal)
Alternative label
  • Rinzivillo S.; Pedreschi D.; Nanni M.; Giannotti F.; Andrienko N.; Andrienko G. (2008)
    Visually-driven analysis of movement data by progressive clustering
    in Information visualization (Print)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Rinzivillo S.; Pedreschi D.; Nanni M.; Giannotti F.; Andrienko N.; Andrienko G. (literal)
Pagina inizio
  • 225 (literal)
Pagina fine
  • 239 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Information Visualization, vol. 7 (3/4) pp. 225 - 239. Palgrave MacMillan, 2008. (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Dipartimento di Informatica, Università di Pisa, Fraunhofer IAIS (literal)
Titolo
  • Visually-driven analysis of movement data by progressive clustering (literal)
Abstract
  • The paper investigates the possibilities of using clustering techniques in visual exploration and analysis of large numbers of trajectories, i.e. sequences of time-stamped locations of some moving entities. Trajectories are complex spatio-temporal constructs characterized by diverse non-trivial properties. To assess the degree of (dis)similarity between trajectories, specific methods (distance functions) are required. A single distance function accounting for all properties of trajectories, first, is difficult to build, second, would require much time to compute, third, might be difficult to understand and to use. We suggest the procedure of progressive clustering where a simple distance function with a clear meaning is applied on each step, which leads to easily interpretable outcomes. Successive application of several different functions enables sophisticated analyses through gradual refinement of earlier obtained results. Besides the advantages from the sense-making perspective, progressive clustering enables a rational work organization where time-consuming computations are applied to relatively small potentially interesting subsets obtained by means of \"cheap\" distance functions producing quick results. We introduce the concept of progressive clustering by an example of analyzing a large real dataset. We also review the existing clustering methods, describe the method OPTICS suitable for progressive clustering of trajectories, and briefly present several distance functions for trajectories. (literal)
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