A Hybrid Grid-based Method for Mining Arbitrary Regions-of-Interest from Trajectories

Authors:
Chihiro Hio;Luke Bermingham;Guochen Cai;Kyungmi Lee;Ickjai Lee
Affiliations:
School of Business (IT), James Cook University, Cairns, QLD4870, Australia;School of Business (IT), James Cook University, Cairns, QLD4870, Australia;School of Business (IT), James Cook University, Cairns, QLD4870, Australia;School of Business (IT), James Cook University, Cairns, QLD4870, Australia;School of Business (IT), James Cook University, Cairns, QLD4870, Australia
Venue:
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
Year:
2013

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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.