A new trajectory clustering algorithm based on TRACLUS

Most trajectory clustering algorithms including the famous TRACLUS require the setting of two input parameters and are sensitive to input parameters. Incorrect setting may cause the algorithm to produce the wrong clusters. Aiming at this vulnerability, we propose a Shielding Parameters Sensitivity T...

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Vydáno v:2012 2nd International Conference on Computer Science and Network Technology s. 783 - 787
Hlavní autor: Jiashun, Chen
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.12.2012
Témata:
Clustering algorithms
Data mining
Depression
High dimensional data
input parameters
Merging
Partitioning algorithms
reachable plot
Sensitivity
Sorting
Trajectory
trajectory clustering
Uncertainty
ISBN:1467329630, 9781467329637
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Abstract Most trajectory clustering algorithms including the famous TRACLUS require the setting of two input parameters and are sensitive to input parameters. Incorrect setting may cause the algorithm to produce the wrong clusters. Aiming at this vulnerability, we propose a Shielding Parameters Sensitivity Trajectory Clustering algorithm named SPSTC. Firstly, we present some definitions about the core distance and reachable distance of line segment, according to which generates cluster sorting. Secondly, reachable plots of line segment sets are constructed according to cluster sorting and reachable distance. Thirdly, parameterized sequence is extracted according to reachable plot, and then the final trajectory clustering based on parameterized sequence is acquired. Parameterized sequence represents inner clustering structure of trajectory data. The experimental results on real and synthetic trajectory data demonstrate that the SPSTC algorithm reduces the sensitivity to input parameters remarkably and improves the efficiency of trajectory clustering while keeps the quality of trajectory clustering.
AbstractList Most trajectory clustering algorithms including the famous TRACLUS require the setting of two input parameters and are sensitive to input parameters. Incorrect setting may cause the algorithm to produce the wrong clusters. Aiming at this vulnerability, we propose a Shielding Parameters Sensitivity Trajectory Clustering algorithm named SPSTC. Firstly, we present some definitions about the core distance and reachable distance of line segment, according to which generates cluster sorting. Secondly, reachable plots of line segment sets are constructed according to cluster sorting and reachable distance. Thirdly, parameterized sequence is extracted according to reachable plot, and then the final trajectory clustering based on parameterized sequence is acquired. Parameterized sequence represents inner clustering structure of trajectory data. The experimental results on real and synthetic trajectory data demonstrate that the SPSTC algorithm reduces the sensitivity to input parameters remarkably and improves the efficiency of trajectory clustering while keeps the quality of trajectory clustering.
Author Jiashun, Chen
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Snippet Most trajectory clustering algorithms including the famous TRACLUS require the setting of two input parameters and are sensitive to input parameters. Incorrect...
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SubjectTerms Clustering algorithms
Data mining
Depression
High dimensional data
input parameters
Merging
Partitioning algorithms
reachable plot
Sensitivity
Sorting
Trajectory
trajectory clustering
Uncertainty
Title A new trajectory clustering algorithm based on TRACLUS
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