Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/108591
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Type: Conference paper
Title: Clustering subtrajectories of moving objects based on a distance metric with multi-dimensional weights
Author: Chen, Y.
Shen, H.
Tian, H.
Citation: Proceedings - International Symposium on Parallel Architectures, Algorithms and Programming, PAAP, 2014, pp.203-208
Publisher: IEEE
Publisher Place: Online
Issue Date: 2014
ISBN: 9781479938445
ISSN: 2168-3034
2168-3042
Conference Name: Sixth International Symposium on Parallel Architectures, Algorithms and Programming (PAAP) (13 Jul 2014 - 15 Jul 2014 : Beijing, China)
Statement of
Responsibility: 
Yanjun Chen, Hong Shen, Hui Tian
Abstract: Mining spatio-temporal data has recently gained great interest due to the integration of wireless communications and positioning technologies. Although clustering spatio-temporal data as a popular mining task has been well studied, the problem of properly defining the distance between the objects to make the clustering results suit the application needs still remainslargely unsolved. In this paper, for the purpose for trajectory data processing we propose an improved trajectory segmentation algorithm and a new object distance metric that considers multiple dimensions on the characteristics of moving object’s subtrajectories. Then, we use the new distance metric in a varient of the existing fuzzy clustering algorithm to improve the quality of clustering results. The experimental evaluation over real world trajectory data record with GPS demonstrates the efficiency and effectiveness of our approach.
Keywords: Spatio-temporal data mining; trajectory clustering; trajectory segmentation; FCM
Rights: © 2014 IEEE
DOI: 10.1109/PAAP.2014.59
Published version: http://dx.doi.org/10.1109/paap.2014.59
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Computer Science publications

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