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Sensors 2017, 17(9), 2013; doi:10.3390/s17092013

An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis

1
College of Computer and Information, Hohai University, Nanjing 210098, China
2
School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou 213032, China
*
Author to whom correspondence should be addressed.
Received: 14 July 2017 / Revised: 29 August 2017 / Accepted: 30 August 2017 / Published: 2 September 2017
(This article belongs to the Special Issue Sensors for Transportation)
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Abstract

Clustering analysis is one of the most important issues in trajectory data mining. Trajectory clustering can be widely applied in the detection of hotspots, mobile pattern analysis, urban transportation control, and hurricane prediction, etc. To obtain good clustering performance, the existing trajectory clustering approaches need to input one or more parameters to calibrate the optimal values, which results in a heavy workload and computational complexity. To realize adaptive parameter calibration and reduce the workload of trajectory clustering, an adaptive trajectory clustering approach based on the grid and density (ATCGD) is proposed in this paper. The proposed ATCGD approach includes three parts: partition, mapping, and clustering. In the partition phase, ATCGD applies the average angular difference-based MDL (AD-MDL) partition method to ensure the partition accuracy on the premise that it decreases the number of the segments after the partition. During the mapping procedure, the partitioned segments are mapped into the corresponding cells, and the mapping relationship between the segments and the cells are stored. In the clustering phase, adopting the DBSCAN-based method, the segments in the cells are clustered on the basis of the calibrated values of parameters from the mapping procedure. The extensive experiments indicate that although the results of the adaptive parameter calibration are not optimal, in most cases, the difference between the adaptive calibration and the optimal is less than 5%, while the run time of clustering can reduce about 95%, compared with the TRACLUS algorithm. View Full-Text
Keywords: mobile pattern analysis; spatio-temporal data; trajectory clustering; adaptive parameter calibration; grid mobile pattern analysis; spatio-temporal data; trajectory clustering; adaptive parameter calibration; grid
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Mao, Y.; Zhong, H.; Qi, H.; Ping, P.; Li, X. An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis. Sensors 2017, 17, 2013.

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