On Discovering Moving Clusters in Spatio-temporal Data

  • Kalnis P
  • Mamoulis N
  • Bakiras S
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Amoving cluster is defined by a set of objects thatmove close to each other for a long time interval. Real-life examples are a group of migrating ani- mals, a convoy of cars moving in a city, etc. We study the discovery of moving clusters in a database of object trajectories. The difference of this problem com- pared to clustering trajectories and mining movement patterns is that the identity of amoving cluster remains unchanged while its location and contentmay change over time. For example, while a group of animals are migrating, some animals may leave the group or new animals may enter it.We provide a formal definition for moving clusters and describe three algorithms for their automatic discovery: (i) a straight-forward method based on the definition, (ii) a more efficientmethod which avoids redundant checks and (iii) an approximate algorithm which trades accuracy for speed by borrowing ideas from the MPEG-2 video encoding. The experimental results demonstrate the efficiency of our techniques and their appli- cability to large spatio-temporal datasets. 1

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  • Panos Kalnis

  • Nikos Mamoulis

  • Spiridon Bakiras

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