Cited By
View all- Cai GLee KLee I(2016)Discovering Common Semantic Trajectories from Geo-tagged Social MediaTrends in Applied Knowledge-Based Systems and Data Science10.1007/978-3-319-42007-3_27(320-332)Online publication date: 14-Jul-2016
A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories
Pages 5 - 12
Abstract
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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Index Terms
- A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories
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- Dept. of Information Science, Univ.of Otago: Department of Information Science, University of Otago, Dunedin, New Zealand
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Association for Computing Machinery
New York, NY, United States
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Published: 02 December 2013
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MLSDA '13
MLSDA '13: Workshop on Machine Learning for Sensory Data Analysis
December 2, 2013
Dunedin, New Zealand
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MLSDA '13 Paper Acceptance Rate 8 of 11 submissions, 73%;
Overall Acceptance Rate 8 of 11 submissions, 73%
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View all- Cai GLee KLee I(2016)Discovering Common Semantic Trajectories from Geo-tagged Social MediaTrends in Applied Knowledge-Based Systems and Data Science10.1007/978-3-319-42007-3_27(320-332)Online publication date: 14-Jul-2016
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