Clustering Geometrically-Modeled Points in the Aggregated Uncertainty Model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00555605" target="_blank" >RIV/67985807:_____/21:00555605 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.3233/FI-2021-2097" target="_blank" >http://dx.doi.org/10.3233/FI-2021-2097</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3233/FI-2021-2097" target="_blank" >10.3233/FI-2021-2097</a>

Result language
angličtina
Original language name
Clustering Geometrically-Modeled Points in the Aggregated Uncertainty Model
Original language description
The k-center problem is to choose a subset of size k from a set of n points such that the maximum distance from each point to its nearest center is minimized. Let Q = {Q1,... , Qn} be a set of polygons or segments in the region-based uncertainty model, in which each Qi is an uncertain point, where the exact locations of the points in Qi are unknown. The geometric objects such as segments and polygons can be models of a point set. We define the uncertain version of the k-center problem as a generalization in which the objective is to find k points from Q to cover the remaining regions of Q with minimum or maximum radius of the cluster to cover at least one or all exact instances of each Qi, respectively. We modify the region-based model to allow multiple points to be chosen from a region, and call the resulting model the aggregated uncertainty model. All these problems contain the point version as a special case, so they are all NP-hard with a lower bound 1.822 for the approximation factor. We give approximation algorithms for uncertain k-center of a set of segments and polygons. We also have implemented some of our algorithms on a data-set to show our theoretical performance guarantees can be achieved in practice.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GJ19-06792Y" target="_blank" >GJ19-06792Y: Structural properties of visibility in terrains and farthest color Voronoi diagrams</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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