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<titleInfo><title>Dynamic clustering to minimize the sum of radii</title></titleInfo>


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<name type="personal">
  <namePart type="given">Monika H</namePart>
  <namePart type="family">Henzinger</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">540c9bbd-f2de-11ec-812d-d04a5be85630</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-5008-6530</description></name>
<name type="personal">
  <namePart type="given">Dariusz</namePart>
  <namePart type="family">Leniowski</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Claire</namePart>
  <namePart type="family">Mathieu</namePart>
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<abstract lang="eng">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.</abstract>

<originInfo><publisher>Springer Nature</publisher><dateIssued encoding="w3cdtf">2020</dateIssued>
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<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Algorithmica</title></titleInfo>
  <identifier type="issn">0178-4617</identifier>
  <identifier type="eIssn">1432-0541</identifier>
  <identifier type="arXiv">1707.02577</identifier><identifier type="doi">10.1007/s00453-020-00721-7</identifier>
<part><detail type="volume"><number>82</number></detail><detail type="issue"><number>11</number></detail><extent unit="pages">3183-3194</extent>
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<mla>Henzinger, Monika, et al. “Dynamic Clustering to Minimize the Sum of Radii.” &lt;i&gt;Algorithmica&lt;/i&gt;, vol. 82, no. 11, Springer Nature, 2020, pp. 3183–94, doi:&lt;a href=&quot;https://doi.org/10.1007/s00453-020-00721-7&quot;&gt;10.1007/s00453-020-00721-7&lt;/a&gt;.</mla>
<short>M. Henzinger, D. Leniowski, C. Mathieu, Algorithmica 82 (2020) 3183–3194.</short>
<ieee>M. Henzinger, D. Leniowski, and C. Mathieu, “Dynamic clustering to minimize the sum of radii,” &lt;i&gt;Algorithmica&lt;/i&gt;, vol. 82, no. 11. Springer Nature, pp. 3183–3194, 2020.</ieee>
<ista>Henzinger M, Leniowski D, Mathieu C. 2020. Dynamic clustering to minimize the sum of radii. Algorithmica. 82(11), 3183–3194.</ista>
<chicago>Henzinger, Monika, Dariusz Leniowski, and Claire Mathieu. “Dynamic Clustering to Minimize the Sum of Radii.” &lt;i&gt;Algorithmica&lt;/i&gt;. Springer Nature, 2020. &lt;a href=&quot;https://doi.org/10.1007/s00453-020-00721-7&quot;&gt;https://doi.org/10.1007/s00453-020-00721-7&lt;/a&gt;.</chicago>
<apa>Henzinger, M., Leniowski, D., &amp;#38; Mathieu, C. (2020). Dynamic clustering to minimize the sum of radii. &lt;i&gt;Algorithmica&lt;/i&gt;. Springer Nature. &lt;a href=&quot;https://doi.org/10.1007/s00453-020-00721-7&quot;&gt;https://doi.org/10.1007/s00453-020-00721-7&lt;/a&gt;</apa>
<ama>Henzinger M, Leniowski D, Mathieu C. Dynamic clustering to minimize the sum of radii. &lt;i&gt;Algorithmica&lt;/i&gt;. 2020;82(11):3183-3194. doi:&lt;a href=&quot;https://doi.org/10.1007/s00453-020-00721-7&quot;&gt;10.1007/s00453-020-00721-7&lt;/a&gt;</ama>
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