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Intell. Syst. Technol."],"published-print":{"date-parts":[[2022,6,30]]},"abstract":"<jats:p>\n            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\u2019s daily lives. How to accurately aggregate large-scale trajectories is an important and challenging task. Conventional clustering algorithms (e.g.,\n            <jats:italic>k<\/jats:italic>\n            -means or\n            <jats:italic>k<\/jats:italic>\n            -mediods) cannot be directly employed to process trajectory data due to their serialization, triviality and redundancy. Aiming to overcome the drawbacks of traditional\n            <jats:italic>k<\/jats:italic>\n            -means algorithm and\n            <jats:italic>k<\/jats:italic>\n            -mediods, including their sensitivity to the selection of the initial\n            <jats:italic>k<\/jats:italic>\n            value, the cluster centers and easy convergence to a locally optimal solution, we first propose an optimized\n            <jats:italic>k<\/jats:italic>\n            -means algorithm (namely\n            <jats:italic>OKM<\/jats:italic>\n            ) to obtain\n            <jats:italic>k<\/jats:italic>\n            optimal initial clustering centers based on the density of trajectory points. Second, because\n            <jats:italic>k<\/jats:italic>\n            -means is sensitive to noisy points, we propose an improved\n            <jats:italic>k<\/jats:italic>\n            -mediods algorithm called\n            <jats:italic>IKMD<\/jats:italic>\n            based on an acceptable radius\n            <jats:italic>r<\/jats:italic>\n            by considering users\u2019 geographic location in LBSNs. The value of\n            <jats:italic>k<\/jats:italic>\n            can be calculated based on\n            <jats:italic>r<\/jats:italic>\n            , and the optimal\n            <jats:italic>k<\/jats:italic>\n            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\n            <jats:italic>IKMD<\/jats:italic>\n            by comparing it with the commonly used clustering approaches through illustrative examples. Last, we conduct extensive experiments to evaluate the performance of\n            <jats:italic>IKMD<\/jats:italic>\n            against seven clustering approaches including the proposed optimized\n            <jats:italic>k<\/jats:italic>\n            -means algorithm,\n            <jats:italic>k<\/jats:italic>\n            -mediods algorithm, traditional density-based\n            <jats:italic>k<\/jats:italic>\n            -mediods algorithm and the state-of-the-arts trajectory clustering methods. The results demonstrate that\n            <jats:italic>IKMD<\/jats:italic>\n            significantly outperforms existing algorithms in the cost of distance calculation and the convergence speed. The methods proposed is proved to contribute to a larger effort targeted at advancing the study of intelligent trajectory data analytics.\n          <\/jats:p>","DOI":"10.1145\/3480972","type":"journal-article","created":{"date-parts":[[2022,3,3]],"date-time":"2022-03-03T09:07:01Z","timestamp":1646298421000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Algorithms for Trajectory Points Clustering in Location-based Social Networks"],"prefix":"10.1145","volume":"13","author":[{"given":"Nan","family":"Han","sequence":"first","affiliation":[{"name":"Chengdu University of Information Technology, Chendu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaojie","family":"Qiao","sequence":"additional","affiliation":[{"name":"Chengdu University of Information Technology, Chendu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kun","family":"Yue","sequence":"additional","affiliation":[{"name":"Yunnan University, Kunming, Yunnan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbin","family":"Huang","sequence":"additional","affiliation":[{"name":"Xidian University, Xi\u2019an, Shanxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"He","sequence":"additional","affiliation":[{"name":"Swinburne University of Technology Melbourne, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Tang","sequence":"additional","affiliation":[{"name":"Chengdu University of Information Technology, Chendu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faliang","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanning Normal University Nanning, Nanning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunlin","family":"He","sequence":"additional","affiliation":[{"name":"China West Normal University Nanchong, Nanchong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-An","family":"Yuan","sequence":"additional","affiliation":[{"name":"Guangxi College of Education Nanning, Nanning, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,3,3]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"1027","volume-title":"Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA\u201907)","author":"Arthur David","year":"2007","unstructured":"David Arthur and Sergei Vassilvitskii. 2007. k-means++: The advantages of careful seeding. 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