A hybrid grid-based method for mining arbitrary regions-of-interest from trajectories
Conference Publication ResearchOnline@JCUAbstract
There is an increasing need for a trajectory pattern mining as the volume of available trajectory data grows at an unprecedented rate with the aid of mobile sensing. Region-of-interest mining identifies interesting hot spots that reveal trajectory concentrations. This article introduces an efficient and effective grid-based region-of-interest mining method that is linear to the number of grid cells, and is able to detect arbitrary shapes of regions-of-interest. The proposed algorithm is robust and applicable to continuous and discrete trajectories, and relatively insensitive to parameter values. Experiments show promising results which demonstrate benefits of the proposed algorithm.
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Publication Name
Workshop on Machine Learning for Sensory Data Analysis
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ISBN/ISSN
978-1-4503-2513-4
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Pages Count
8
Location
Dunedin, New Zealand
Publisher
Association for Computing Machinery
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Publisher Location
New York, NY, USA
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DOI
10.1145/2542652.2542653