Articolo in rivista, 2006, ENG, 10.1007/s10844-006-9953-7
Nanni M.; Pedreschi D.
CNR-ISTI, Pisa, Italy; Dipartimento di Informatica - Università di Pisa, Pisa, Italy
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.
Journal of intelligent information systems 27 (3), pp. 267–289
OPTICS, Spatio-temporal data mining, Trajectory clustering
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 182243
Year: 2006
Type: Articolo in rivista
Creation: 2012-04-26 18:36:27.000
Last update: 2023-07-17 16:16:29.000
CNR authors
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:182243
DOI: 10.1007/s10844-006-9953-7
Scopus: 2-s2.0-37849187329
ISI Web of Science (WOS): 000243498200005