research-article

Algorithms for Trajectory Points Clustering in Location-based Social Networks

Authors:

Chang-An YuanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 13, Issue 3

Article No.: 43, Pages 1 - 29

https://doi.org/10.1145/3480972

Published: 03 March 2022 Publication History

Abstract

Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents. This has further increased the popularity of location-based social networks (LBSNs) and produces a huge amount of trajectories composed of continuous and complex spatio-temporal points from people’s daily lives. How to accurately aggregate large-scale trajectories is an important and challenging task. Conventional clustering algorithms (e.g., k-means or k-mediods) cannot be directly employed to process trajectory data due to their serialization, triviality and redundancy. Aiming to overcome the drawbacks of traditional k-means algorithm and k-mediods, including their sensitivity to the selection of the initial k value, the cluster centers and easy convergence to a locally optimal solution, we first propose an optimized k-means algorithm (namely OKM) to obtain k optimal initial clustering centers based on the density of trajectory points. Second, because k-means is sensitive to noisy points, we propose an improved k-mediods algorithm called IKMD based on an acceptable radius r by considering users’ geographic location in LBSNs. The value of k can be calculated based on r, and the optimal k points are selected as the initial clustering centers with high densities to reduce the cost of distance calculation. Thirdly, we thoroughly analyze the advantages of IKMD by comparing it with the commonly used clustering approaches through illustrative examples. Last, we conduct extensive experiments to evaluate the performance of IKMD against seven clustering approaches including the proposed optimized k-means algorithm, k-mediods algorithm, traditional density-based k-mediods algorithm and the state-of-the-arts trajectory clustering methods. The results demonstrate that IKMD significantly outperforms existing algorithms in the cost of distance calculation and the convergence speed. The methods proposed is proved to contribute to a larger effort targeted at advancing the study of intelligent trajectory data analytics.

References

[1]

David Arthur and Sergei Vassilvitskii. 2007. k-means++: The advantages of careful seeding. In Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA’07), Nikhil Bansal, Kirk Pruhs, and Clifford Stein (Eds.). SIAM, 1027–1035.

[2]

Sivadi Balakrishna, M. Thirumaran, R. Padmanaban, and Vijender Kumar Solanki. 2020. An efficient incremental clustering based improved K-Medoids for IoT multivariate data cluster analysis. Peer Peer Netw. Appl. 13, 4 (2020), 1152–1175.

[3]

Hancheng Cao, Fengli Xu, Jagan Sankaranarayanan, Yong Li, and Hanan Samet. 2020. Habit2vec: Trajectory semantic embedding for living pattern recognition in population. IEEE Trans. Mobile Comput. 19, 5 (2020), 1096–1108.

[4]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD’96). AAAI Press, 226–231.

Digital Library

[5]

Douglas H. Fisher. 1987. Knowledge acquisition via incremental conceptual clustering. Mach. Learn. 2, 2 (1987), 139–172.

[6]

Sudipto Guha, Rajeev Rastogi, and Kyuseok Shim. 2001. Cure: An efficient clustering algorithm for large databases. Inf. Syst. 26, 1 (2001), 35–58.

Digital Library

[7]

Chih-Chieh Hung, Wen-Chih Peng, and Wang-Chien Lee. 2015. Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. VLDB J. 24, 2 (2015), 169–192.

Digital Library

[8]

Leonard Kaufman and Peter J. Rousseeuw. 1990. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley.

[9]

Jae-Gil Lee, Jiawei Han, and Kyu-Young Whang. [n.d.]. Trajectory clustering: A partition-and-group framework. In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM, 593–604.

[10]

Lei Li, Kai Zheng, Sibo Wang, Wen Hua, and Xiaofang Zhou. 2018. Go slow to go fast: minimal on-road time route scheduling with parking facilities using historical trajectory. VLDB J. 27, 3 (2018), 321–345.

Digital Library

[11]

Tianyi Li, Ruikai Huang, Lu Chen, Christian S. Jensen, and Torben Bach Pedersen. 2020. Compression of uncertain trajectories in road networks. Proc. VLDB Endow. 13, 7 (2020), 1050–1063.

Digital Library

[12]

David Liben-Nowell, Jasmine Novak, Ravi Kumar, Prabhakar Raghavan, and Andrew Tomkins. 2005. Geographic routing in social networks. Proc. Natl. Acad. Sci. U.S.A. 102, 33 (2005), 11623–11628.

[13]

Caihong Liu and Chonghui Guo. 2020. STCCD: Semantic trajectory clustering based on community detection in networks. Expert Syst. Appl. 162 (2020), 113689. https://www.sciencedirect.com/science/article/abs/pii/S0957417420305133.

[14]

James MacQueen et al. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1. Oakland, CA, USA, 281–297.

[15]

Jiali Mao, Qiuge Song, Cheqing Jin, Zhigang Zhang, and Aoying Zhou. 2018. Online clustering of streaming trajectories. Front. Comput. Sci. 12, 2 (2018), 245–263.

Digital Library

[16]

Xinzheng Niu, Ting Chen, Chase Q. Wu, Jiajun Niu, and Yuran Li. 2020. Label-based trajectory clustering in complex road networks. IEEE Trans. Intell. Transp. Syst. 21, 10 (2020), 4098–4110.

[17]

Bharat Pardeshi and Durga Toshniwal. 2010. Improved k-medoids clustering based on cluster validity index and object density. In Proceedings of the IEEE 2nd International Advance Computing Conference (IACC’10).

[18]

Hae-Sang Park and Chi-Hyuck Jun. 2009. A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36, 2 (2009), 3336–3341.

Digital Library

[19]

Shaojie Qiao, Nan Han, Yunjun Gao, Rong-Hua Li, Jianbin Huang, Jun Guo, Louis Alberto Gutierrez, and Xindong Wu. 2018. A fast parallel community discovery model on complex networks through approximate optimization. IEEE Trans. Knowl. Data Eng. 30, 9 (2018), 1638–1651.

Digital Library

[20]

Shaojie Qiao, Nan Han, Junfeng Wang, Rong-Hua Li, Louis Alberto Gutierrez, and Xindong Wu. 2018. Predicting long-term trajectories of connected vehicles via the prefix-projection technique. IEEE Trans. Intell. Transport. Syst. 19, 7 (2018), 2305–2315.

[21]

Shaojie Qiao, Nan Han, Jiliu Zhou, Rong-Hua Li, Cheqing Jin, and Louis Alberto Gutierrez. 2018. SocialMix: A familiarity-based and preference-aware location suggestion approach. Eng. Appl. Artif. Intell. 68 (2018), 192–204. https://www.sciencedirect.com/science/article/abs/pii/S0952197617302907.

Digital Library

[22]

Shaojie Qiao, Nan Han, William Zhu, and Louis Alberto Gutierrez. 2015. TraPlan: An effective three-in-one trajectory-prediction model in transportation networks. IEEE Trans. Intell. Transport. Syst. 16, 3 (2015), 1188–1198.

Digital Library

[23]

Shaojie Qiao, Dayong Shen, Xiaoteng Wang, Nan Han, and William Zhu. 2015. A self-adaptive parameter selection trajectory prediction approach via hidden markov models. IEEE Trans. Intell. Transport. Syst. 16, 1 (2015), 284–296.

Digital Library

[24]

Shaojie Qiao, Changjie Tang, Huidong Jin, Teng Long, Shucheng Dai, Yungchang Ku, and Michael Chau. 2010. PutMode: Prediction of uncertain trajectories in moving objects databases. Appl. Intell. 33, 3 (2010), 370–386.

Digital Library

[25]

Maayan Roth, Assaf Ben-David, David Deutscher, Guy Flysher, Ilan Horn, Ari Leichtberg, Naty Leiser, Yossi Matias, and Ron Merom. 2010. Suggesting friends using the implicit social graph. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 233–242.

Digital Library

[26]

Han Su, Guanglin Cong, Wei Chen, Bolong Zheng, and Kai Zheng. 2019. Personalized route description based on historical trajectories. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM’19). ACM, 79–88.

Digital Library

[27]

Han Su, Shuncheng Liu, Bolong Zheng, Xiaofang Zhou, and Kai Zheng. 2020. A survey of trajectory distance measures and performance evaluation. VLDB J. 29, 1 (2020), 3–32.

Digital Library

[28]

Xiujuan Sun and Xiyu Liu. 2008. New genetic K-means clustering algorthm based on meliorated initial center. Comput. Eng. Appl.23 (2008), 170–172+186.

[29]

Yuichiro Takeuchi and Masanori Sugimoto. 2005. An outdoor recommendation system based on user location history. In Proceedings of the 1st Internaltional Workshop on Personalized Context Modeling and Management for UbiComp Applications (ubiPCMM’05).

[30]

Ji Tang, Linfeng Liu, Jiagao Wu, Jian Zhou, and Yang Xiang. 2020. Trajectory clustering method based on spatial-temporal properties for mobile social networks. J. Intell. Inf. Syst. 56, 3 (2021), 73–95.

[31]

Shoko Wakamiya, Ryong Lee, and Kazutoshi Sumiya. 2011. Urban area characterization based on semantics of crowd activities in Twitter. In GeoSpatial Semantics, Christophe Claramunt, Sergei Levashkin, and Michela Bertolotto (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg.

[32]

Hao Wang, Manolis Terrovitis, and Nikos Mamoulis. 2013. Location recommendation in location-based social networks using user check-in data. In Proceedings of the 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL’13). 364–373.

Digital Library

[33]

Sheng Wang, Zhifeng Bao, J. Shane Culpepper, Timos Sellis, and Xiaolin Qin. 2019. Fast large-scale trajectory clustering. Proc. VLDB Endow. 13, 1 (2019), 29–42.

Digital Library

[34]

Wei Wang, Jiong Yang, and Richard R. Muntz. 1997. STING: A statistical information grid approach to spatial data mining. In Proceedings of 23rd International Conference on Very Large Data Bases (VLDB’97). Morgan Kaufmann, 186–195.

[35]

Zhu Wang, Daqing Zhang, Dingqi Yang, Zhiyong Yu, and Xingshe Zhou. 2012. Detecting overlapping communities in location-based social networks. In Proceedings of the 4th International Conference on Social Informatics (SocInfo’12). 110–123.

Digital Library

[36]

Yuqing Yang, Jianghui Cai, Haifeng Yang, Jifu Zhang, and Xujun Zhao. 2020. TAD: A trajectory clustering algorithm based on spatial-temporal density analysis. Expert Syst. Appl. 139, 1 (2020), 112846.1–112846.16.

[37]

Yun Yang and Jianmin Jiang. 2018. Bi-weighted ensemble via HMM-based approaches for temporal data clustering. Pattern Recogn. 76 (2018), 391–403. https://www.sciencedirect.com/science/article/abs/pii/S0031320317304764.

Digital Library

[38]

Donghua Yu, Guojun Liu, Maozu Guo, and Xiaoyan Liu. 2018. An improved K-medoids algorithm based on step increasing and optimizing medoids. Expert Syst. Appl. 92 (2018), 464–473. https://www.sciencedirect.com/science/article/abs/pii/S0957417417306589.

Digital Library

[39]

Guan Yuan, Penghui Sun, Jie Zhao, Daxing Li, and Canwei Wang. 2017. A review of moving object trajectory clustering algorithms. Artif. Intell. Rev. 47, 1 (2017), 123–144.

Digital Library

[40]

Tianzhu Zhang, Hanqing Lu, and Stan Z. Li. 2009. Learning semantic scene models by object classification and trajectory clustering. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’09). IEEE Computer Society, 1940–1947.

[41]

K. Zheng, Y. Zhao, D. Lian, B. Zheng, G. Liu, and X. Zhou. 2020. Reference-based framework for spatio-temporal trajectory compression and query processing. IEEE Trans. Knowl. Data Eng. 32, 11 (2020), 2227–2240.

[42]

Yu Zheng, Lizhu Zhang, Zhengxin Ma, Xing Xie, and Wei-Ying Ma. 2011. Recommending friends and locations based on individual location history. ACM Trans. Web 5, 1 (2011), 5:1–5:44.

Digital Library

Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Qiao SHan NLi HYuan GWu TPeng YCai HHuang J(2024)A three-in-one dynamic shared bicycle demand forecasting model under non-classical conditionsApplied Intelligence10.1007/s10489-024-05607-754:17-18(8592-8611)Online publication date: 1-Jul-2024
https://doi.org/10.1007/s10489-024-05607-7
Wang WXie Y(2022)Multi-Level Clustering Algorithm for Pedestrian Trajectory Flow Considering Multi-Camera Information2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI)10.1109/CEI57409.2022.9950091(691-698)Online publication date: 23-Sep-2022
https://doi.org/10.1109/CEI57409.2022.9950091
Show More Cited By

Index Terms

Algorithms for Trajectory Points Clustering in Location-based Social Networks
1. Information systems
  1. Information systems applications
    1. Mobile information processing systems
    2. Spatial-temporal systems
2. Social and professional topics
  1. User characteristics
    1. Geographic characteristics

Recommendations

Trajectory clustering: a partition-and-group framework
SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data

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 ...
A new hybrid method based on partitioning-based DBSCAN and ant clustering

Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based ...
Density-based semi-supervised clustering

Semi-supervised clustering methods guide the data partitioning and grouping process by exploiting background knowledge, among else in the form of constraints. In this study, we propose a semi-supervised density-based clustering method. Density-based ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 3

June 2022

415 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3508465

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

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].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 March 2022

Accepted: 01 August 2021

Revised: 01 April 2021

Received: 01 January 2021

Published in TIST Volume 13, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

National Natural Science Foundation of China
Sichuan Science and Technology Program
Chengdu Technology Innovation and Research and Development Project
Chengdu Major Science and Technology Innovation Project
Chengdu ”Take the lead” Science and Technology Project
Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music, Chengdu, China
Guangdong Basic and Applied Basic Research Foundation

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
805
Total Downloads

Downloads (Last 12 months)66
Downloads (Last 6 weeks)6

Reflects downloads up to 12 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Wu YWang ZLi YGuo YJiang HZhu XWu X(2024)Co-occurrence Order-preserving Pattern Mining with Keypoint Alignment for Time SeriesACM Transactions on Management Information Systems10.1145/365845015:2(1-27)Online publication date: 12-Jun-2024
https://dl.acm.org/doi/10.1145/3658450
Qiao SHan NLi HYuan GWu TPeng YCai HHuang J(2024)A three-in-one dynamic shared bicycle demand forecasting model under non-classical conditionsApplied Intelligence10.1007/s10489-024-05607-754:17-18(8592-8611)Online publication date: 1-Jul-2024
https://doi.org/10.1007/s10489-024-05607-7
Wang WXie Y(2022)Multi-Level Clustering Algorithm for Pedestrian Trajectory Flow Considering Multi-Camera Information2022 2nd International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI)10.1109/CEI57409.2022.9950091(691-698)Online publication date: 23-Sep-2022
https://doi.org/10.1109/CEI57409.2022.9950091
Eljabu LEtemad MMatwin S(2022)Spatial Clustering Method of Historical AIS Data for Maritime Traffic Routes Extraction2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020646(893-902)Online publication date: 17-Dec-2022
https://doi.org/10.1109/BigData55660.2022.10020646

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View full text|Download PDF

View Issue’s Table of Contents