A hybrid grid-based method for mining arbitrary regions-of-interest from trajectories

Hio, Chihiro, Bermingham, Luke, Cai, Guochen, Lee, Kyungmi, and Lee, Ickjai (2013) A hybrid grid-based method for mining arbitrary regions-of-interest from trajectories. In: Proceedings of Workshop on Machine Learning for Sensory Data Analysis, pp. 5-12. From: Workshop on Machine Learning for Sensory Data Analysis, 3 December 2013, Dunedin, New Zealand.

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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.

Item ID: 31281
Item Type: Conference Item (Refereed Research Paper - E1)
Keywords: regions-of-interest, trajectories, arbitrary shape, clustering
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ISBN: 978-1-4503-2513-4
Date Deposited: 18 Feb 2014 06:37
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%
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