{"date_published":"2020-11-01T00:00:00Z","publisher":"Springer Nature","year":"2020","article_type":"original","day":"01","issue":"11","oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.1707.02577"}],"scopus_import":"1","quality_controlled":"1","title":"Dynamic clustering to minimize the sum of radii","intvolume":" 82","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"status":"public","page":"3183-3194","external_id":{"arxiv":["1707.02577"]},"citation":{"ista":"Henzinger MH, Leniowski D, Mathieu C. 2020. Dynamic clustering to minimize the sum of radii. Algorithmica. 82(11), 3183–3194.","chicago":"Henzinger, Monika H, Dariusz Leniowski, and Claire Mathieu. “Dynamic Clustering to Minimize the Sum of Radii.” Algorithmica. Springer Nature, 2020. https://doi.org/10.1007/s00453-020-00721-7.","apa":"Henzinger, M. H., Leniowski, D., & Mathieu, C. (2020). Dynamic clustering to minimize the sum of radii. Algorithmica. Springer Nature. https://doi.org/10.1007/s00453-020-00721-7","mla":"Henzinger, Monika H., et al. “Dynamic Clustering to Minimize the Sum of Radii.” Algorithmica, vol. 82, no. 11, Springer Nature, 2020, pp. 3183–94, doi:10.1007/s00453-020-00721-7.","ama":"Henzinger MH, Leniowski D, Mathieu C. Dynamic clustering to minimize the sum of radii. Algorithmica. 2020;82(11):3183-3194. doi:10.1007/s00453-020-00721-7","short":"M.H. Henzinger, D. Leniowski, C. Mathieu, Algorithmica 82 (2020) 3183–3194.","ieee":"M. H. Henzinger, D. Leniowski, and C. Mathieu, “Dynamic clustering to minimize the sum of radii,” Algorithmica, vol. 82, no. 11. Springer Nature, pp. 3183–3194, 2020."},"month":"11","date_created":"2022-07-27T13:58:58Z","article_processing_charge":"No","publication_status":"published","publication":"Algorithmica","abstract":[{"text":"In this paper, we study the problem of opening centers to cluster a set of clients in a metric space so as to minimize the sum of the costs of the centers and of the cluster radii, in a dynamic environment where clients arrive and depart, and the solution must be updated efficiently while remaining competitive with respect to the current optimal solution. We call this dynamic sum-of-radii clustering problem. We present a data structure that maintains a solution whose cost is within a constant factor of the cost of an optimal solution in metric spaces with bounded doubling dimension and whose worst-case update time is logarithmic in the parameters of the problem.","lang":"eng"}],"_id":"11674","author":[{"last_name":"Henzinger","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","full_name":"Henzinger, Monika H","first_name":"Monika H","orcid":"0000-0002-5008-6530"},{"first_name":"Dariusz","full_name":"Leniowski, Dariusz","last_name":"Leniowski"},{"full_name":"Mathieu, Claire","first_name":"Claire","last_name":"Mathieu"}],"doi":"10.1007/s00453-020-00721-7","publication_identifier":{"eissn":["1432-0541"],"issn":["0178-4617"]},"date_updated":"2022-09-12T08:50:14Z","volume":82,"extern":"1","type":"journal_article"}