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|Title:||On discovering moving clusters in spatio-temporal data|
|Authors:||Kalnis, P. |
|Citation:||Kalnis, P.,Mamoulis, N.,Bakiras, S. (2005). On discovering moving clusters in spatio-temporal data. Lecture Notes in Computer Science 3633 : 364-381. ScholarBank@NUS Repository.|
|Abstract:||A moving cluster is defined by a set of objects that move close to each other for a long time interval. Real-life examples are a group of migrating animals, 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 compared to clustering trajectories and mining movement patterns is that the identity of a moving cluster remains unchanged while its location and content may 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 efficient method 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 applicability to large spatio-temporal datasets. © Springer-Verlag Berlin Heidelberg 2005.|
|Source Title:||Lecture Notes in Computer Science|
|Appears in Collections:||Staff Publications|
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