Using Relative Distance and Hausdorff Distance to Mine Trajectory Clusters

Along with development of location service and GPS technology, mining information from trajectory datasets becomes one of hottest research topic in data mining. How to efficiently mine the clusters from trajectories attract more and more researchers. In this paper, a new framework of trajectory clus...

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Main Authors: Guan, Bo (Author), Liu, Liangxu (Author), Chen, Jinyang (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2013-01-10.
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042 |a dc 
100 1 0 |a Guan, Bo  |e author 
100 1 0 |e contributor 
700 1 0 |a Liu, Liangxu  |e author 
700 1 0 |a Chen, Jinyang  |e author 
245 0 0 |a Using Relative Distance and Hausdorff Distance to Mine Trajectory Clusters 
260 |b Institute of Advanced Engineering and Science,   |c 2013-01-10. 
520 |a Along with development of location service and GPS technology, mining information from trajectory datasets becomes one of hottest research topic in data mining. How to efficiently mine the clusters from trajectories attract more and more researchers. In this paper, a new framework of trajectory clustering, called Trajectory Clustering based Improved Minimum Hausdorff Distance under Translation (TraClustMHD) is proposed. In this framework, the distance between two trajectory segments based on local and relative distance is defined. And then, traditional clusters algorithm is employed to mine the clusters of trajectory segment. In additional, R-Tree is employed to improve the efficiency. The experimental results showed that our algorithm better than existing others which are based on Hausdorff distance and based on line Hausdorff distance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.1877 
540 |a Copyright (c) 2013 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc-nd/4.0 
546 |a eng 
690
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
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786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 11, No 1: January 2013; 115-122 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v11.i1 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/1942/782 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/1942/782  |z Get fulltext