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ISPRS Int. J. Geo-Inf. 2018, 7(5), 164; https://doi.org/10.3390/ijgi7050164

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

1
School of Information and Engineering, Sichuan Tourism University, Chengdu 610100, China
2
Key Lab of Earth Exploration & Information Techniques of Ministry Education, Chengdu University of Technology, Chengdu 610059, China
3
School of Mathematics and Computer Science, Aba Teachers University, Wenchuan 623002, China
4
School of Computer Science, Chengdu University of Information Technology, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Received: 11 March 2018 / Revised: 21 April 2018 / Accepted: 23 April 2018 / Published: 25 April 2018

Abstract

Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory. View Full-Text
Keywords: trajectory regression clustering; Hausdorff distance; angle-based line segments partitioning; Lagrange-based fuzzy C-means; least squares regression; taxi GPS data trajectory regression clustering; Hausdorff distance; angle-based line segments partitioning; Lagrange-based fuzzy C-means; least squares regression; taxi GPS data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Zhou, X.; Miao, F.; Ma, H.; Zhang, H.; Gong, H. A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method. ISPRS Int. J. Geo-Inf. 2018, 7, 164.

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