research-article

Finding frequent sub-trajectories with time constraints

Authors:

Xin WangAuthors Info & Claims

UrbComp '13: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

Article No.: 13, Pages 1 - 8

https://doi.org/10.1145/2505821.2505824

Published: 11 August 2013 Publication History

Abstract

With the advent of location-based social media and location-acquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. Trajectory pattern mining has received a lot of attention in recent years. Frequent sub-trajectories, in particular, might contain very usable knowledge. In this paper, we define a new trajectory pattern called frequent sub-trajectories with time constraints (FSTTC) that requires not only the same continuous location sequence but also the similar staying time in each location. We present a two-phase approach to find FSTTCs based on suffix tree. Firstly, we select the spatial information from the trajectories and generate location sequences. Then the suffix tree is adopted to mine out the frequent location sequences. Secondly, we cluster all sub-trajectories with the same frequent location sequence with respect to the staying time using modified DBSCAN algorithm to find the densest clusters. Accordingly, the frequent sub-trajectories with time constraints, represented by the clusters, are identified. Experimental results show that our approach is efficient and can find useful and interesting information from the spatio-temporal trajectories.

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Cited By

Wang PChiu HChen WChang CHuang YHuang TLai YChang CZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)TraVisProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357848(2881-2884)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357848
Badretdinov RTakhavova EShleimovich M(2019)Characteristic Trajectories Detection in Spatio-Temporal Data Streams2019 International Science and Technology Conference "EastConf"10.1109/EastConf.2019.8725376(1-5)Online publication date: Mar-2019
https://doi.org/10.1109/EastConf.2019.8725376
Huang XChen HZheng MLiu PQian J(2014)Trajectory Pattern Mining: Methods and ApplicationsApplied Mechanics and Materials10.4028/www.scientific.net/AMM.490-491.1361490-491(1361-1367)Online publication date: Jan-2014
https://doi.org/10.4028/www.scientific.net/AMM.490-491.1361

Index Terms

Finding frequent sub-trajectories with time constraints
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

UrbComp '13: Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing

August 2013

135 pages

ISBN:9781450323314

DOI:10.1145/2505821

General Chairs:
Steven E. Koonin
New York University
,
Ouri E. Wolfson
University of Illinois at Chicago
,
Program Chair:
Yu Zheng
Microsoft Research Asia

Copyright © 2013 ACM.

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Publication History

Published: 11 August 2013

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KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 11, 2013

Illinois, Chicago

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Cited By

Wang PChiu HChen WChang CHuang YHuang TLai YChang CZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)TraVisProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357848(2881-2884)Online publication date: 3-Nov-2019
https://dl.acm.org/doi/10.1145/3357384.3357848
Badretdinov RTakhavova EShleimovich M(2019)Characteristic Trajectories Detection in Spatio-Temporal Data Streams2019 International Science and Technology Conference "EastConf"10.1109/EastConf.2019.8725376(1-5)Online publication date: Mar-2019
https://doi.org/10.1109/EastConf.2019.8725376
Huang XChen HZheng MLiu PQian J(2014)Trajectory Pattern Mining: Methods and ApplicationsApplied Mechanics and Materials10.4028/www.scientific.net/AMM.490-491.1361490-491(1361-1367)Online publication date: Jan-2014
https://doi.org/10.4028/www.scientific.net/AMM.490-491.1361

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