Tutorial on Spatial and Spatio-Temporal Data Mining Part II ... - UFSC

SummaryThese data mining approaches deal with Trajectory SamplesTid geometry timest1 48.890018 2.246100 08:251 48.890018 2.246100 08:26... ... ...1 48.890020 2.246102 08:401 48.888880 2.248208 08:411 48.885732 2.255031 08:42... ... ...1 48.858434 2.336105 09:041 48.853611 2.349190 09:05... ... ...1 48.853610 2.349205 09:401 48.860515 2.349018 09:41... ... ...1 48.861112 2.334167 10:001 48.861531 2.336018 10:011 48.861530 2.336020 10:02... ... ...2 ... ...ReferencesLaube, P. and Imfeld, S. (2002). Analyzing relative motion within groups of trackablemoving point objects. In Egenhofer, M. J. and Mark, D. M., editors, GIScience, volume2478 of Lecture Notes in Computer Science, pages 132–144. Springer.Laube, P., Imfeld, S., and Weibel, R. (2005a). Discovering relative motion patterns ingroups of moving point objects. International Journal of Geographical InformationScience, 19(6):639–668.Laube, P., van Kreveld, M., and Imfeld, S. (2005b). Finding REMO: Detecting RelativeMotion Patterns in Geospatial Lifelines. Springer.Lee, J.-G., Han, J., and Whang, K.-Y. (2007). Trajectory clustering: a partition-and-groupframework. In Chan, C. Y., Ooi, B. C., and Zhou, A., editors, SIGMOD Conference,pages 593–604. ACM.Li, Y., Han, J., and Yang, J. (2004). Clustering moving objects. In KDD ’04: Proceedings ofthe tenth ACM SIGKDD international conference on Knowledge discovery and datamining, pages 617–622, New York, NY, USA. ACM Press.Nanni, M. and Pedreschi, D. (2006). Time-focused clustering of trajectories of movingobjects. Journal of Intelligent Information Systems, 27(3):267–289.