Data Mining Algorithm for Moving Object Trajectory
Author:
Affiliation:
1. Information Cernet, Jinzhong University, Jinzhong, 030600 China
2. Department of Information technology and Engineering, Jinzhong University, Jinzhong, 030600 China
Abstract
Publisher
North Atlantic University Union (NAUN)
Subject
Electrical and Electronic Engineering,Signal Processing
Reference14 articles.
1. C. C. Hung, W. C. Peng, and W. C. Lee, “Clustering and aggregating clues of trajectories for mining trajectory patterns and routes,” The VLDB Journal, vol. 24, no. 2, pp. 169-192, 2015.
2. M. Li, X. Li, and J. Yin, “TORD problem and its solution based on big trajectories data,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1666-1677, 2016.
3. C. Ruhhammer, M. Baumann, and V. Protschky, H. Kloeden, F. Klanner, and C. Stiller, “Automated intersection mapping from crowd trajectory data,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 3, pp. 1-12, 2017.
4. G. Yuan, P. Sun, and J. Zhao, “A review of moving object trajectory clustering algorithms,” Artificial Intelligence Review, vol. 47, no. 1, pp. 123-144, 2017.
5. L. Wang, K. Y. Hu, and T. Ku, “Mining urban moving tra jectory patterns based on multi-scale space partition and road network modeling,” Acta Automatica Sinica, vol. 41, no. 1, pp. 47-58, 2015.
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