Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories

Xiaoliang Geng, Takeaki Uno, Hiroki Arimura

Abstract

In this paper, we implement recent theoretical progress of depth-first algorithms for mining flock pat-terns (Arimura et al., 2013) based on depth-first frequent itemset mining approach, such as Eclat (Zaki, 2000) or LCM (Uno et al., 2004). Flock patterns are a class of spatio-temporal patterns that represent a groups of moving objects close each other in a given time segment (Gudmundsson and van Kreveld, Proc. ACM GIS’06; Benkert, Gudmundsson, Hubner, Wolle, Computational Geometry, 41:11, 2008). We implemented two extension of a basic algorithm, one for a class of closed patterns, called rightward length-maximal flock patterns, and the other with a speed-up technique using geometric indexes. To evalute these extensions, we ran experiments on synthesis datasets. The experiments demonstrate that the modified algorithms with the above extensions are several order of magnitude faster than the original algorithm in most parameter settings.

References

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Paper Citation


in Harvard Style

Geng X., Uno T. and Arimura H. (2013). Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013) ISBN 978-989-8565-75-4, pages 143-151. DOI: 10.5220/0004543401430151


in Bibtex Style

@conference{kdir13,
author={Xiaoliang Geng and Takeaki Uno and Hiroki Arimura},
title={Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)},
year={2013},
pages={143-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004543401430151},
isbn={978-989-8565-75-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge Management and Information Sharing - Volume 1: KDIR, (IC3K 2013)
TI - Trajectory Pattern Mining in Practice - Algorithms for Mining Flock Patterns from Trajectories
SN - 978-989-8565-75-4
AU - Geng X.
AU - Uno T.
AU - Arimura H.
PY - 2013
SP - 143
EP - 151
DO - 10.5220/0004543401430151