Reference Work Entry

Encyclopedia of GIS

pp 1168-1173

Trajectories, Discovering Similar

  • George KolliosAffiliated withDepartment of Computer Science, Boston University
  • , Michail VlachosAffiliated withIBM T.J. Watson Research Center
  • , Dimitris GunopulosAffiliated withDepartment of Computer Science and Engineering, University of California


Multi-dimensional time series similarity; Mining spatio-temporal datasets


The trajectory of a moving object is typically modeled as a sequence of consecutive locations in a multi‐dimensional (generally two or three dimensional) Euclidean space. Such data types arise in many applications where the location of a given object is measured repeatedly over time. Typical trajectory data are obtained during a tracking procedure with the aid of various sensors. Here also lies the main obstacle of such data; they may contain a significant amount of outliers or in other words incorrect data measurements (unlike for example, stock data which contain no errors whatsoever). An example of two trajectories is shown in Fig. 1.
Figure 1

Examples of 2D trajectories

Many data mining tasks, such as clustering and classification, necessitate a distance function that is used to estimate the similarity or dis‐similarity between any two objects in the database. F ...

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