Approximating Fair $k$-Min-Sum-Radii in Euclidean Space Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.48550/arxiv.2309.00834
The $k$-center problem is a classical clustering problem in which one is asked to find a partitioning of a point set $P$ into $k$ clusters such that the maximum radius of any cluster is minimized. It is well-studied. But what if we add up the radii of the clusters instead of only considering the cluster with maximum radius? This natural variant is called the $k$-min-sum-radii problem. It has become the subject of more and more interest in recent years, inspiring the development of approximation algorithms for the $k$-min-sum-radii problem in its plain version as well as in constrained settings. We study the problem for Euclidean spaces $\mathbb{R}^d$ of arbitrary dimension but assume the number $k$ of clusters to be constant. In this case, a PTAS for the problem is known (see Bandyapadhyay, Lochet and Saurabh, SoCG, 2023). Our aim is to extend the knowledge base for $k$-min-sum-radii to the domain of fair clustering. We study several group fairness constraints, such as the one introduced by Chierichetti et al. (NeurIPS, 2017). In this model, input points have an additional attribute (e.g., colors such as red and blue), and clusters have to preserve the ratio between different attribute values (e.g., have the same fraction of red and blue points as the ground set). Different variants of this general idea have been studied in the literature. To the best of our knowledge, no approximative results for the fair $k$-min-sum-radii problem are known, despite the immense amount of work on the related fair $k$-center problem. We propose a PTAS for the fair $k$-min-sum-radii problem in Euclidean spaces of arbitrary dimension for the case of constant $k$. To the best of our knowledge, this is the first PTAS for the problem. It works for different notions of group fairness.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.00834
- https://arxiv.org/pdf/2309.00834
- OA Status
- green
- Related Works
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- OpenAlex ID
- https://openalex.org/W4386552427
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386552427Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.00834Digital Object Identifier
- Title
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Approximating Fair $k$-Min-Sum-Radii in Euclidean SpaceWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-02Full publication date if available
- Authors
-
Lukas Drexler, Annika Hennes, A. Lahiri, Melanie Schmidt, Julian WargallaList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.00834Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.00834Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.00834Direct OA link when available
- Concepts
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RADIUS, Combinatorics, Mathematics, Dimension (graph theory), Cluster (spacecraft), Constant (computer programming), Euclidean distance, Base (topology), Euclidean geometry, Domain (mathematical analysis), Cluster analysis, k-means clustering, Discrete mathematics, Mathematical analysis, Geometry, Computer science, Statistics, Computer security, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.SoCG, | 135 |
| abstract_inverted_index.asked | 12 |
| abstract_inverted_index.case, | 122 |
| abstract_inverted_index.first | 281 |
| abstract_inverted_index.group | 156, 292 |
| abstract_inverted_index.input | 173 |
| abstract_inverted_index.known | 129 |
| abstract_inverted_index.plain | 91 |
| abstract_inverted_index.point | 19 |
| abstract_inverted_index.radii | 45 |
| abstract_inverted_index.ratio | 192 |
| abstract_inverted_index.set). | 210 |
| abstract_inverted_index.study | 100, 154 |
| abstract_inverted_index.which | 9 |
| abstract_inverted_index.works | 287 |
| abstract_inverted_index.(e.g., | 179, 197 |
| abstract_inverted_index.2017). | 169 |
| abstract_inverted_index.2023). | 136 |
| abstract_inverted_index.Lochet | 132 |
| abstract_inverted_index.amount | 242 |
| abstract_inverted_index.assume | 111 |
| abstract_inverted_index.become | 68 |
| abstract_inverted_index.blue), | 185 |
| abstract_inverted_index.called | 62 |
| abstract_inverted_index.colors | 180 |
| abstract_inverted_index.domain | 149 |
| abstract_inverted_index.extend | 141 |
| abstract_inverted_index.ground | 209 |
| abstract_inverted_index.known, | 238 |
| abstract_inverted_index.model, | 172 |
| abstract_inverted_index.number | 113 |
| abstract_inverted_index.points | 174, 206 |
| abstract_inverted_index.radius | 29 |
| abstract_inverted_index.recent | 77 |
| abstract_inverted_index.spaces | 105, 262 |
| abstract_inverted_index.values | 196 |
| abstract_inverted_index.years, | 78 |
| abstract_inverted_index.between | 193 |
| abstract_inverted_index.cluster | 32, 54 |
| abstract_inverted_index.despite | 239 |
| abstract_inverted_index.general | 215 |
| abstract_inverted_index.immense | 241 |
| abstract_inverted_index.instead | 49 |
| abstract_inverted_index.maximum | 28, 56 |
| abstract_inverted_index.natural | 59 |
| abstract_inverted_index.notions | 290 |
| abstract_inverted_index.problem | 2, 7, 88, 102, 127, 236, 259 |
| abstract_inverted_index.propose | 252 |
| abstract_inverted_index.radius? | 57 |
| abstract_inverted_index.related | 247 |
| abstract_inverted_index.results | 231 |
| abstract_inverted_index.several | 155 |
| abstract_inverted_index.studied | 219 |
| abstract_inverted_index.subject | 70 |
| abstract_inverted_index.variant | 60 |
| abstract_inverted_index.version | 92 |
| abstract_inverted_index.Saurabh, | 134 |
| abstract_inverted_index.clusters | 24, 48, 116, 187 |
| abstract_inverted_index.constant | 270 |
| abstract_inverted_index.fairness | 157 |
| abstract_inverted_index.fraction | 201 |
| abstract_inverted_index.interest | 75 |
| abstract_inverted_index.preserve | 190 |
| abstract_inverted_index.problem. | 65, 250, 285 |
| abstract_inverted_index.variants | 212 |
| abstract_inverted_index.(NeurIPS, | 168 |
| abstract_inverted_index.Different | 211 |
| abstract_inverted_index.Euclidean | 104, 261 |
| abstract_inverted_index.arbitrary | 108, 264 |
| abstract_inverted_index.attribute | 178, 195 |
| abstract_inverted_index.classical | 5 |
| abstract_inverted_index.constant. | 119 |
| abstract_inverted_index.different | 194, 289 |
| abstract_inverted_index.dimension | 109, 265 |
| abstract_inverted_index.fairness. | 293 |
| abstract_inverted_index.inspiring | 79 |
| abstract_inverted_index.knowledge | 143 |
| abstract_inverted_index.settings. | 98 |
| abstract_inverted_index.$k$-center | 1, 249 |
| abstract_inverted_index.additional | 177 |
| abstract_inverted_index.algorithms | 84 |
| abstract_inverted_index.clustering | 6 |
| abstract_inverted_index.introduced | 163 |
| abstract_inverted_index.knowledge, | 228, 277 |
| abstract_inverted_index.minimized. | 34 |
| abstract_inverted_index.clustering. | 152 |
| abstract_inverted_index.considering | 52 |
| abstract_inverted_index.constrained | 97 |
| abstract_inverted_index.development | 81 |
| abstract_inverted_index.literature. | 222 |
| abstract_inverted_index.Chierichetti | 165 |
| abstract_inverted_index.constraints, | 158 |
| abstract_inverted_index.partitioning | 16 |
| abstract_inverted_index.approximation | 83 |
| abstract_inverted_index.approximative | 230 |
| abstract_inverted_index.well-studied. | 37 |
| abstract_inverted_index.$\mathbb{R}^d$ | 106 |
| abstract_inverted_index.Bandyapadhyay, | 131 |
| abstract_inverted_index.$k$-min-sum-radii | 64, 87, 146, 235, 258 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.16876544 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |