An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Author:

Wang Xin1ORCID,Niu Xinzheng1ORCID,Zhu Jiahui1ORCID,Liu Zuoyan2ORCID

Affiliation:

1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

2. Department of Rehabilitation Medical Center, West China Hospital, West China School of Nursing, Sichuan University, Chengdu, China

Abstract

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

Funder

Ministry of Education of the People's Republic of China

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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