Article

Trajectory clustering: a partition-and-group framework

Authors:

Kyu-Young WhangAuthors Info & Claims

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

Pages 593 - 604

https://doi.org/10.1145/1247480.1247546

Published: 11 June 2007 Publication History

Abstract

Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.

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Du YChen KYi GYu WXian ZSong W(2024)A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic TrajectoriesRemote Sensing10.3390/rs1611194416:11(1944)Online publication date: 28-May-2024
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Index Terms

Trajectory clustering: a partition-and-group framework
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

June 2007

1210 pages

ISBN:9781595936868

DOI:10.1145/1247480

General Chairs:
Lizhu Zhou
Tsinghua University, China
,
Tok Wang Ling
National University of Singapore, Singapore
,
Program Chair:
Beng Chin Ooi
National University of Singapore, Singapore

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 June 2007

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SIGMOD/PODS07

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SIGMOD/PODS07: International Conference on Management of Data

June 11 - 14, 2007

Beijing, China

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://doi.org/10.1587/transfun.2023EAP1132
Du YChen KYi GYu WXian ZSong W(2024)A New Trajectory Clustering Method for Mining Multiple Periodic Patterns from Complex Oceanic TrajectoriesRemote Sensing10.3390/rs1611194416:11(1944)Online publication date: 28-May-2024
https://doi.org/10.3390/rs16111944
Wang ZHe WLan JZhu CLei JLiu X(2024)Ship Trajectory Classification Prediction at Waterway Confluences: An Improved KNN ApproachJournal of Marine Science and Engineering10.3390/jmse1207107012:7(1070)Online publication date: 26-Jun-2024
https://doi.org/10.3390/jmse12071070
Kaklis DKontopoulos IVarlamis IEmiris IVarelas T(2024)Trajectory Mining and Routing: A Cross-Sectoral ApproachJournal of Marine Science and Engineering10.3390/jmse1201015712:1(157)Online publication date: 12-Jan-2024
https://doi.org/10.3390/jmse12010157
Reyes GTolozano-Benites RLanzarini LEstrebou CBariviera ABarzola-Monteses J(2024)Method for the Identification and Classification of Zones with Vehicular CongestionISPRS International Journal of Geo-Information10.3390/ijgi1303007313:3(73)Online publication date: 28-Feb-2024
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Zhang MZhang LLiu T(2024)Aircraft Behavior Recognition on Trajectory Data with a Multimodal ApproachElectronics10.3390/electronics1302036713:2(367)Online publication date: 16-Jan-2024
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Kennedy LZüfle A(2024)Kinematic Detection of Anomalies in Human Trajectory DataProceedings of the 1st ACM SIGSPATIAL International Workshop on Geospatial Anomaly Detection10.1145/3681765.3698464(94-97)Online publication date: 29-Oct-2024
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Jiang FYan H(2024)Research on an improved ship trajectory clustering methodInternational Conference on Smart Transportation and City Engineering (STCE 2023)10.1117/12.3024768(232)Online publication date: 14-Feb-2024
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Xie GXu HXu JZhao SLi YWang CHu XTian Y(2024)Sequential Trajectory Data Publishing With Adaptive Grid-Based Weighted Differential PrivacyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.344943336:12(9249-9262)Online publication date: Dec-2024
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