TY - JOUR
T1 - On a clustering-based mining approach with labeled semantics for significant place discovery
AU - Niu, Xinzheng
AU - Wang, Shimin
AU - Wu, Chase Q.
AU - Li, Yuran
AU - Wu, Peng
AU - Zhu, Jiahui
N1 - Funding Information:
This research is sponsored by the Science and Technology Planning Project of Sichuan Province under Grant No. 2020YFG0054, and the Scientific Research Project of State Grid Sichuan Electric Power Company Information and Communication Company under Grant No. SGSCXT00XGJS1800219. We would also like to thank Bowen Shi for his comments, which helped improve part of this research.
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/11
Y1 - 2021/11
N2 - With the rapid increase in GPS data collection through pervasive use of mobile devices, it has become an important problem to discover significant places of moving objects from complex spatial and temporal trajectories. This problem is challenging mainly because such trajectory data suffer from several issues including incompleteness, low quality, high redundancy, and oftentimes trajectory points do not follow Gaussian distribution. We propose a clustering-based method with temporal and spatial semantics, referred to as Stops and Moves of Trajectories using Attribute Selection (SMoTAS), whose technical advantages are multifold. Firstly, it improves data availability by using a self-adaptive algorithm to correct the deviation in traditional speed-based methods. Secondly, it improves place mining accuracy by filtering multi-label clustering results when there is a lack of detailed geographic data. Thirdly, it employs feature selection to exploit the core attributes of clustering and simplify the clustering results with Grubbs criterion. Experimental results on real-life datasets show that SMoTAS not only achieves substantial improvement of accuracy over existing methods in discovering significant places, but also exhibits superior adaptability to different trajectories and application scenarios.
AB - With the rapid increase in GPS data collection through pervasive use of mobile devices, it has become an important problem to discover significant places of moving objects from complex spatial and temporal trajectories. This problem is challenging mainly because such trajectory data suffer from several issues including incompleteness, low quality, high redundancy, and oftentimes trajectory points do not follow Gaussian distribution. We propose a clustering-based method with temporal and spatial semantics, referred to as Stops and Moves of Trajectories using Attribute Selection (SMoTAS), whose technical advantages are multifold. Firstly, it improves data availability by using a self-adaptive algorithm to correct the deviation in traditional speed-based methods. Secondly, it improves place mining accuracy by filtering multi-label clustering results when there is a lack of detailed geographic data. Thirdly, it employs feature selection to exploit the core attributes of clustering and simplify the clustering results with Grubbs criterion. Experimental results on real-life datasets show that SMoTAS not only achieves substantial improvement of accuracy over existing methods in discovering significant places, but also exhibits superior adaptability to different trajectories and application scenarios.
KW - Feature selection
KW - Semantic trajectory
KW - Significant place discovery
KW - Spatio-temporal trajectory clustering
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U2 - 10.1016/j.ins.2021.07.050
DO - 10.1016/j.ins.2021.07.050
M3 - Article
AN - SCOPUS:85110590100
SN - 0020-0255
VL - 578
SP - 37
EP - 63
JO - Information Sciences
JF - Information Sciences
ER -