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Trajectories Modeling and Clustering
Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 20181 The location of moving objects facilitates the monitoring of their evolution and the history of displacement offers interesting perspectives in the field of the study of the behavior of these objects. The purpose of this paper is to provide a new methodology for constructing object trajectories based on a matching process that implements different ...
Boutaina Hdioud +2 more
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Clustering uncertain trajectories
Knowledge and Information Systems, 2010Knowledge discovery in Trajectory Databases (TD) is an emerging field which has recently gained great interest. On the other hand, the inherent presence of uncertainty in TD (e.g., due to GPS errors) has not been taken yet into account during the mining process.
Nikos Pelekis +4 more
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007
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 is very useful in many applications, especially if we have regions of special interest for analysis.
Lee, Jae-Gil +2 more
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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 is very useful in many applications, especially if we have regions of special interest for analysis.
Lee, Jae-Gil +2 more
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Multi-granularity Visualization of Trajectory Clusters Using Sub-trajectory Clustering
2009 IEEE International Conference on Data Mining Workshops, 2009With the surging of the requirements of location-based services, mining various interesting patterns from the spatial data becomes more and more important. In this paper, we propose an approach for visualizing the trajectory clustering results based on sub-trajectory clusters discovered from large-scale trajectory data.
Cheng Chang, Baoyao Zhou
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Online clustering of streaming trajectories
Frontiers of Computer Science, 2018With the increasing availability of modern mobile devices and location acquisition technologies, massive trajectory data of moving objects are collected continuously in a streaming manner. Clustering streaming trajectories facilitates finding the representative paths or common moving trends shared by different objects in real time. Although data stream
Jiali Mao +4 more
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A General Methodology for n-Dimensional Trajectory Clustering
Expert Systems With Applications, 2015 Trajectory data is rich in dimensionality, often containing valuable patterns in more than just the spatial and temporal dimensions. Yet existing trajectory clustering techniques only consider a fixed number of dimensions. We propose a general trajectory
Luke Bermingham, Ickjai Lee
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Trajectory kinematics descriptor for trajectory clustering in surveillance videos
2015 IEEE International Symposium on Circuits and Systems (ISCAS), 2015Trajectories provide spatial-temporal information of foreground objects for event clustering and analysis. Because of the kinematic properties of foreground objects, the lengths of trajectories will be different which lead to the length problem of assessing similarity between two or more trajectories. To solve the problem, we propose a novel descriptor
Wei-Cheng Wang +3 more
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Fast large-scale trajectory clustering
Proceedings of the VLDB Endowment, 2019In this paper, we study the problem of large-scale trajectory data clustering,k-paths, which aims to efficiently identifyk"representative" paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters,k-paths can be used for visual exploration in applications such as traffic monitoring, public ...
Sheng Wang 0007 +4 more
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A Complete Framework for Clustering Trajectories
2009 21st IEEE International Conference on Tools with Artificial Intelligence, 2009The increasing availability of huge amounts of thin data, i.e. data pertaining to time and positions generated by different sources with a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatio-temporal data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from
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Non-separable Transforms for Clustering Trajectories
2011Trajectory data refer to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks). Mining such amounts of data is challenging, since the possibility to extract useful information from these peculiar kind of data is crucial in many application scenarios such as vehicle ...
Alfredo Cuzzocrea, Elio Masciari
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