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Algorithms for Trajectory Points Clustering in Location-based Social Networks
Published: 03 March 2022 Publication History
ACM Transactions on Intelligent Systems and Technology Volume 13, Issue 3
Published: 3 March 2022 Accepted: 1 August 2021 Revised: 1 April 2021 Received: 1 January 2021
Qualifiers research-article Refereed
Funding Sources National Natural Science Foundation of China Sichuan Science and Technology Program Chengdu Technology Innovation and Research and Development Project Chengdu Major Science and Technology Innovation Project Chengdu ”Take the lead” Science and Technology Project Digital Media Art, Key Laboratory of Sichuan Province, Sichuan Conservatory of Music, Chengdu, China Guangdong Basic and Applied Basic Research Foundation
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Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents. This has further increased the popularity of location-based social networks (LBSNs) and produces a huge amount of trajectories composed of continuous and complex spatio-temporal points from people’s daily lives. How to accurately aggregate large-scale trajectories is an important and challenging task. Conventional clustering algorithms (e.g., k -means or k -mediods) cannot be directly employed to process trajectory data due to their serialization, triviality and redundancy. Aiming to overcome the drawbacks of traditional k -means algorithm and k -mediods, including their sensitivity to the selection of the initial k value, the cluster centers and easy convergence to a locally optimal solution, we first propose an optimized k -means algorithm (namely OKM ) to obtain k optimal initial clustering centers based on the density of trajectory points. Second, because k -means is sensitive to noisy points, we propose an improved k -mediods algorithm called IKMD based on an acceptable radius r by considering users’ geographic location in LBSNs. The value of k can be calculated based on r , and the optimal k points are selected as the initial clustering centers with high densities to reduce the cost of distance calculation. Thirdly, we thoroughly analyze the advantages of IKMD by comparing it with the commonly used clustering approaches through illustrative examples. Last, we conduct extensive experiments to evaluate the performance of IKMD against seven clustering approaches including the proposed optimized k -means algorithm, k -med
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