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Context-based location clustering and prediction using conditional random fields

Published: 25 November 2014 Publication History

Abstract

The embrace of pervasive devices accounts for the production of a massive amount of location data. While multitudes of algorithms have been used for location clustering, most of them focus on the proximity clustering of locations rather than on their location contexts. In this work, we propose a novel context-based location clustering technique that clusters locations with similar context by solely using raw GPS data from multi-user trajectories. We introduce a new similarity measure that infers the location context and utilize the inferred contexts during clustering. In addition, we propose a predictive model that employs Conditional Random Fields (CRF), context-based location clusters and social ties for future location prediction. We show the strength and efficiency of our techniques through numerous experiments on two real datasets. Our empirical evaluations demonstrate that our approach performs better than a state-of-the-art work.

References

[1]
Daniel Ashbrook and Thad Starner. Using gps to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput., October 2003.
[2]
Hong Cheng, Jihang Ye, and Zhe Zhu. What's your next move: User activity prediction in location-based social networks. In SDM, 2013.
[3]
Eunjoon Cho, Seth A. Myers, and Jure Leskovec. Friendship and mobility: User movement in location-based social networks. In KDD '11, 2011.
[4]
Nathan Eagle and Alex Pentland. Reality mining: sensing complex social systems. In Pers Ubi Comp'06.
[5]
Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD'96.
[6]
Hoyoung Jeung, Qing Liu, Heng Tao Shen, and Xiaofang Zhou. A hybrid prediction model for moving objects. In ICDE'08, 2008.
[7]
Hoyoung Jeung, Man Lung Yiu, Xiaofang Zhou, Christian S. Jensen, and Heng Tao Shen. Discovery of convoys in trajectory databases. VLDB, 2008.
[8]
John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML '01, 2001.
[9]
Zhenhui Li, Bolin Ding, Jiawei Han, and Roland Kays. Swarm: Mining relaxed temporal moving object clusters. Proc. VLDB Endow., 2010.
[10]
Ulrike Luxburg. A tutorial on spectral clustering. Statistics and Computing, 2007.
[11]
Marina Meila and Jianbo Shi. A random walks view of spectral segmentation. In AISTATS'01, 2001.
[12]
Anna Monreale, Fabio Pinelli, Roberto Trasarti, and Fosca Giannotti. Wherenext: a location predictor on trajectory pattern mining. In KDD '09.
[13]
Andrew Y. Ng, Michael I. Jordan, and Yair Weiss. On spectral clustering: Analysis and an algorithm. In NIPS'01, 2001.
[14]
Salvatore Scellato, Mirco Musolesi, Cecilia Mascolo, Vito Latora, and Andrew T. Campbell. Nextplace: a spatio-temporal prediction framework for pervasive systems. In Pervasive'11, 2011.
[15]
Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 2000.
[16]
Hao Wang, Manolis Terrovitis, and Nikos Mamoulis. Location recommendation in location-based social networks using user check-in data. In SIGSPATIAL'13, 2013.
[17]
Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. Exploiting geographical inuence for collaborative point-of-interest recommendation. In SIGIR '11, 2011.
[18]
Yu Zheng, Lizhu Zhang, Xing Xie, and Wei-Ying Ma. Mining correlation between locations using human location history. In GIS '09, pages 472--475, 2009.

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  • (2024)Future locations prediction with multi-graph attention networks based on spatial–temporal LSTM frameworkThe Journal of Supercomputing10.1007/s11227-024-06249-980:14(20020-20041)Online publication date: 29-May-2024
  • (2022)Discovering Actual Delivery Locations from Mis-Annotated Couriers' Trajectories2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00307(3241-3253)Online publication date: May-2022
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    cover image ACM Other conferences
    MUM '14: Proceedings of the 13th International Conference on Mobile and Ubiquitous Multimedia
    November 2014
    275 pages
    ISBN:9781450333047
    DOI:10.1145/2677972
    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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    New York, NY, United States

    Publication History

    Published: 25 November 2014

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    Author Tags

    1. conditional random fields
    2. context-awareness
    3. location clustering
    4. location prediction

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    MUM '14
    MUM '14: International Conference on Mobile and Ubiquitous Multimedia
    November 25 - 28, 2014
    Victoria, Melbourne, Australia

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    Overall Acceptance Rate 190 of 465 submissions, 41%

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    Cited By

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    • (2024)A Complete and Comprehensive Semantic Perception of Mobile Traveling for Mobile Communication ServicesIEEE Internet of Things Journal10.1109/JIOT.2023.330747811:3(5467-5490)Online publication date: 1-Feb-2024
    • (2024)Future locations prediction with multi-graph attention networks based on spatial–temporal LSTM frameworkThe Journal of Supercomputing10.1007/s11227-024-06249-980:14(20020-20041)Online publication date: 29-May-2024
    • (2022)Discovering Actual Delivery Locations from Mis-Annotated Couriers' Trajectories2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00307(3241-3253)Online publication date: May-2022
    • (2022)A Novel Protection Method of Continuous Location Sharing Based on Local Differential Privacy and Conditional Random FieldAlgorithms and Architectures for Parallel Processing10.1007/978-3-030-95384-3_44(710-725)Online publication date: 23-Feb-2022
    • (2022)DLPM: A dynamic location protection mechanism supporting continuous queriesConcurrency and Computation: Practice and Experience10.1002/cpe.749535:19Online publication date: 20-Nov-2022
    • (2019)QUADRIVEN: A Framework for Qualitative Taxi Demand Prediction Based on Time-Variant Online Social Network Data AnalysisSensors10.3390/s1922488219:22(4882)Online publication date: 8-Nov-2019
    • (2019)Identifying locations from geospatial trajectoriesJournal of Computer and System Sciences10.1016/j.jcss.2015.10.00582:4(566-581)Online publication date: 1-Jan-2019
    • (2018)Human mobility semantics analysisGeoinformatica10.5555/3238836.323885922:3(507-539)Online publication date: 1-Jul-2018
    • (2017)Human mobility semantics analysis: a probabilistic and scalable approachGeoInformatica10.1007/s10707-017-0295-022:3(507-539)Online publication date: 10-Apr-2017
    • (2015)Parameter Optimisation for Location Extraction and Prediction Applications2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing10.1109/CIT/IUCC/DASC/PICOM.2015.322(2173-2180)Online publication date: Oct-2015

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