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

• Main Authors: Xiangbing Zhou, Fang Miao, Hongjiang Ma, Hua Zhang, Huaming Gong
• Format: Article
• Language: English
• Published: MDPI AG 2018-04-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: trajectory regression clustering; Hausdorff distance; angle-based line segments partitioning; Lagrange-based fuzzy C-means; least squares regression; taxi GPS data
• Online Access: http://www.mdpi.com/2220-9964/7/5/164

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.