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#### Static and dynamic algorithms for k-point clustering problems

##### MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45509

Smid,  Michiel
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

Datta,  Amitava
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44909

Lenhof,  Hans-Peter
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45433

Schwarz,  Christian
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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##### Fulltext (public)

MPI-I-93-108.pdf
(Any fulltext), 17MB

##### Supplementary Material (public)
There is no public supplementary material available
##### Citation

Smid, M., Datta, A., Lenhof, H.-P., & Schwarz, C.(1993). Static and dynamic algorithms for k-point clustering problems (MPI-I-93-108). Saarbrücken: Max-Planck-Institut für Informatik.

Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-B741-9
##### Abstract
Let $S$ be a set of $n$ points in $d$-space and let $1 \leq k \leq n$ be an integer. A unified approach is given for solving the problem of finding a subset of $S$ of size $k$ that minimizes some closeness measure, such as the diameter, perimeter or the circumradius. Moreover, data structures are given that maintain such a subset under insertions and deletions of points.