Abstract
At present, more and more researchers have focused on the study of the frequent trajectory sequence pattern mining in location-based social network (LBSN), in which the trajectories of contributing frequent patterns in users’ trajectory database must have same or similar the location coordinates and conform to the semantics and time constraints. In this paper, we focus on the study of users’ daily frequent mobile pattern. Excessive limitations on location information may limit the results of mining users’ frequent mobile pattern. Therefore, based on the frequent trajectory sequence pattern mining in LBSNs, we first define a new frequent semantic trajectory sequence pattern mining (FSTS-PM) problem that focuses on the study of mining users’ frequent mobile pattern. FSTS-PM problem does not consider the location coordinates of the trajectory points, but uses the distance and time constraints among the trajectory points in a trajectory sequence to optimize the user’s frequent mobile pattern mining results. Then, we propose the modified PrefixSpan (MP) algorithm which integrates the distance and time filtering mechanism based on the original PrefixSpan to find frequent semantic trajectory sequence pattern. Finally, the extensive experiments verify the performance of MP algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hao, F., Zhang, J., Duan, Z., Zhao, L., Guo, L., Park, D.-S.: Urban area function zoning based on user relationships in location-based social networks. IEEE Access 8, 23487–23495 (2020)
Li, Y., Zhao, X., Zhang, Z., Yuan, Y., Wang, G.: Annotating semantic tags of locations in location-based social networks. GeoInformatica 24(1), 133–152 (2020)
Chen, Y., Zhao, X., Lin, X., Wang, Y., Guo, D.: Efficient mining of frequent patterns on uncertain graphs. IEEE Trans. Knowl. Data Eng. 31(2), 287–300 (2019)
Na Deng, X., Chen, D.L., Xiong, C.: Frequent patterns mining in DNA sequence. IEEE Access 7, 108400–108410 (2019)
Lee, A.J.T., Chen, Y.-A., Ip, W.-C.: Mining frequent trajectory patterns in spatial-temporal databases. Inf. Sci. 179(13), 2218–2231 (2009)
Zhang, C., Han, J., Shou, L., Jiajun, L., La Porta, T.F.: Splitter: mining fine-grained sequential patterns in semantic trajectories. PVLDB 7(9), 769–780 (2014)
Arya, K.K., Goyal, V., Navathe, S.B., Prasad, S.: Mining frequent spatial-textual sequence patterns. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9050, pp. 123–138. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18123-3_8
Pei, J., Han, J., Mortazavi-Asl, B.: Prefixspan: mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224 (2001)
Acknowledgment
This research is partially supported by the National Natural Science Foundation of China under Grant Nos. 61672145, 61702086, China Postdoctoral Science Foundation under Grant No. 2018M631806, Doctor Startup Foundation of Liaoning Province under Grant No. 2020-BS-288, Natural Science Foundation of Liaoning Province of China under Grant No. 20180550260, Scientific Research Foundation of Liaoning Province under Grant Nos. L2019001, L2019003, Doctoral Business and Innavation Launching Plan of Yingkou City under Grant No. QB-2019-16.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Z., Zhang, J., Li, F., Zhao, X., Bi, X. (2020). Frequent Semantic Trajectory Sequence Pattern Mining in Location-Based Social Networks. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-60259-8_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60258-1
Online ISBN: 978-3-030-60259-8
eBook Packages: Computer ScienceComputer Science (R0)