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Original scientific paper
https://doi.org/10.2498/cit.1002578

Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory

Min Ren ; Shandong Normal University, China; Shandong University of Finance and Economics, China; Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, China
Feng Yang ; Shandong University of Finance and Economics, China
Guangchun Zhou ; Shandong University of Finance and Economics, China
Haiping Wang ; Shandong University of Finance and Economics, China

Fulltext: english, pdf (410 KB) pages 245-254 downloads: 593* cite
APA 6th Edition
Ren, M., Yang, F., Zhou, G. & Wang, H. (2015). Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory. Journal of computing and information technology, 23 (3), 245-254. https://doi.org/10.2498/cit.1002578
MLA 8th Edition
Ren, Min, et al. "Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory." Journal of computing and information technology, vol. 23, no. 3, 2015, pp. 245-254. https://doi.org/10.2498/cit.1002578. Accessed 14 May 2021.
Chicago 17th Edition
Ren, Min, Feng Yang, Guangchun Zhou and Haiping Wang. "Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory." Journal of computing and information technology 23, no. 3 (2015): 245-254. https://doi.org/10.2498/cit.1002578
Harvard
Ren, M., et al. (2015). 'Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory', Journal of computing and information technology, 23(3), pp. 245-254. https://doi.org/10.2498/cit.1002578
Vancouver
Ren M, Yang F, Zhou G, Wang H. Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory. Journal of computing and information technology [Internet]. 2015 [cited 2021 May 14];23(3):245-254. https://doi.org/10.2498/cit.1002578
IEEE
M. Ren, F. Yang, G. Zhou and H. Wang, "Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory", Journal of computing and information technology, vol.23, no. 3, pp. 245-254, 2015. [Online]. https://doi.org/10.2498/cit.1002578

Abstracts

This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping time to compute the stops and introduced the quantile function to estimate the value of the parameter, which showed more effectively and accurately than DBSCAN and certain improved DBSCAN algorithms in the experimental results. In addition, after the stops are connected with POIs that have the characteristic of an information presentation, the paper designed a POI-Behavior Mapping Table to analyze the user’s activities according to the stopping time and visiting frequency, on the basis of which the user’s daily regular behavior pattern can be mined from the history trajectories. In the end, LBS operators are able to provide intelligent and personalized services so as to achieve precise marketing in terms of the characteristics of the individual behavior.

Keywords
spatiotemporal data; semantic trajectory; clustering; knowledge discovery

Hrčak ID: 144004

URI
https://hrcak.srce.hr/144004

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