Semantic Trajectory Clustering via Improved Label Propagation With Core Structure

Qiao, Dianfeng; Liang, Yan; Ma, Chaoxiong; Zhang, Huixia

doi:10.1109/jsen.2021.3129200

Cited by 7 publications

(3 citation statements)

References 37 publications

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“…During clustering, Wang et al's suggested trajectory similarity assessment methodology [17] accounted for trajectories' form characteristics in addition to their temporal and spatial components. A semantic trajectory clustering approach that combines the similarity matrix with the k-NN algorithm was outlined by Qiao et al [18]. Xu et al [19] proposed a trajectory clustering algorithm which considers semantic and geographical distances.…”

Section: Background and Related Work 21 Trajectory Clusteringmentioning

confidence: 99%

“…We utilize location data with semantic tagging, as established in our previous research [40,41], given the absence of an approach to group users within the Geolife datasets into diverse clusters. This experiment aims to compare the performance of trajectory clustering and privacy security for ILP [18], BU [42], SP-tree [43], DP-LTOD [19], N-gram [44], and NPT [45], respectively.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Zhu,

Lei,

et al. 2023

Electronics

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Location privacy preserving for location-based social networks (LBSNs) has been attracting a great deal of attention. Existing location privacy protection methods are disadvantaged by issues such as information leakage and low data availability, which are no longer suitable for the current diverse and personalized location-based services. To address these issues, we propose a differential privacy-based spatial-temporal trajectory clustering (DP-STTC) scheme, which mainly transforms the existing location privacy protection mechanism into a spatial-temporal trajectory protection mechanism by adjusting the privacy parameters. Then, the trajectories were clustered to uncover users with similar trajectory characteristics. Finally, experiments were conducted on two real datasets. The experimental results show that our DP-STTC scheme can not only achieve better accuracy in trajectory clustering, but also protect user privacy.

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Section: Background and Related Work 21 Trajectory Clusteringmentioning

confidence: 99%

Section: Evaluation Metricsmentioning

confidence: 99%

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Zhu,

Lei,

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Considering the important role of the semantic layer for target behaviour analysis, scholars have semantically enriched trajectory [6][7][8] and explored semantic representation methods [9][10][11]. On this basis, some scholars performed the cluster analysis [12][13][14] of trajectory points by clustering around discrete semantic information, such as geographic tags [15] and attribute tags [16]. However, the semantic trajectory clustering analysis method did not identify the way that the moving target interacts with the environment, and the recognition of the moving target's behaviour is the key to analyse and judge the target situation.…”

Section: Related Workmentioning

confidence: 99%

A trajectory summarisation generation method based on the mobile robot behaviour analysis

Liu

et al. 2022

IET Cyber-Syst and Robotics

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The semantic representation of the trajectory is conducive to enrich the content of trajectory data mining. A trajectory summarisation generation method based on the mobile robot behaviour analysis was proposed to realize the abstract expression and semantic representation of the spatio-temporal motion features of the robot and its environmental interaction state. First, the behavioural semantic modelling and representation of the mobile robot are completed by modelling the sub-trajectory and calculating the topological behaviour (TOP). Second, Chinese word segmentation and semantic slot filling methods are used to combine with hierarchical clustering to perform basic word extraction and classification for describing trajectory sentences. Then, the description language frame is extracted based on the TOP, and the final trajectory summarisation is generated. The result shows that the proposed method can semantically represent robot behaviours with different motion features and topological features, extract two verb-frameworks for describing the sentences according to their topological features, and dynamically adjust the syntactic structure for the different topological behaviours between the target and the environment. The proposed method can generate semantic information of relatively high quality for spatio-temporal data and help to understand the higher-order semantics of moving robot behaviour.

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Behaviors analysis of on-coming cluster based on knowledge under cluster security requirements

ZHANG,

LIANG,

SHI

et al. 2024

Chinese Journal of Aeronautics

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Semantic Trajectory Clustering via Improved Label Propagation With Core Structure

Cited by 7 publications

References 37 publications

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

Differential Privacy-Based Spatial-Temporal Trajectory Clustering Scheme for LBSNs

A trajectory summarisation generation method based on the mobile robot behaviour analysis

Behaviors analysis of on-coming cluster based on knowledge under cluster security requirements

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