Publication Type:

Journal Article

Source:

Lecture Notes in Computational Vision and Biomechanics, Springer Netherlands, Volume 28, p.215-226 (2018)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042406778&doi=10.1007%2f978-3-319-71767-8_18&partnerID=40&md5=ed0fb38d9ac6243ae52f5084df30751e

Abstract:

<p>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. © 2018, Springer International Publishing AG.</p>

Notes:

cited By 0

Cite this Research Publication

B. A. Sabarish, Karthi, R., and Gireeshkumar, T., “Clustering of trajectory data using hierarchical approaches”, Lecture Notes in Computational Vision and Biomechanics, vol. 28, pp. 215-226, 2018.

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