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Genetic Algorithm Based Hybrid Attribute Selection Using Customized Fitness Function Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Clustering of Trajectory Data Using Hierarchical Approaches

Authors : B. A. Sabarish, R. Karthi, T. Gireeshkumar

Published in: Computational Vision and Bio Inspired Computing

Publisher: Springer International Publishing

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Abstract

Large volume of spatiotemporal data as trajectories are generated from GPS enabled devices such as smartphones, cars, sensors, and social media. In this paper, we present a methodology for clustering of trajectories to identify patterns in vehicle movement. The trajectories are clustered using hierarchical method and similarity between trajectories are computed using Dynamic Time Warping (DTW) measure. We study the effects on clustering by varying the linkage methods used for clustering of trajectories. The clustering method generate clusters that are spatially similar and optimal results are obtained during the clustering process. The results are validated using Cophenetic correlation coefficient, Dunn, and Davies-Bouldin Index by varying the number of clusters. The results are tested for its efficiency using real world data sets. Experimental results demonstrate that hierarchical clustering using DTW measure can cluster trajectories efficiently.

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Metadata
Title
Clustering of Trajectory Data Using Hierarchical Approaches
Authors
B. A. Sabarish
R. Karthi
T. Gireeshkumar
Copyright Year
2018
Book
Computational Vision and Bio Inspired Computing

Print ISBN: 978-3-319-71766-1

Electronic ISBN: 978-3-319-71767-8

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-71767-8_18