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.
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 ...
Reference Work Entry Metrics
- Trajectories, Discovering Similar
- Reference Work Title
- Encyclopedia of GIS
- pp 1168-1173
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Additional Links
- Industry Sectors
- eBook Packages
- Author Affiliations
- 1. Department of Computer Science, Boston University, Boston, MA, USA
- 2. IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
- 3. Department of Computer Science and Engineering, University of California, Riverside, CA, USA
To view the rest of this content please follow the download PDF link above.