Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
1-mean and 1-medoid 2-clustering problem with arbitrary cluster sizes: Complexity and approximation. / Pyatkin, Artem V.
в: Yugoslav Journal of Operations Research, Том 33, № 1, 2023, стр. 59-69.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - 1-mean and 1-medoid 2-clustering problem with arbitrary cluster sizes: Complexity and approximation
AU - Pyatkin, Artem V.
N1 - The research was supported by the program of fundamental scientific researches of the SB RAS, project 0314-2019-0014 and by the Russian Foundation for Basic Research, project 19-01-00308.
PY - 2023
Y1 - 2023
N2 - We consider the following 2-clustering problem. Given N points in Euclidean space, partition it into two subsets (clusters) so that the sum of squared distances between the elements of the clusters and their centers would be minimum. The center of the first cluster coincides with its centroid (mean) while the center of the second cluster should be chosen from the set of the initial points (medoid). It is known that this problem is NP-hard if the cardinalities of the clusters are given as a part of the input. In this paper we prove that the problem remains NP-hard in the case of arbitrary clusters sizes and suggest a 2-approximation polynomial-time algorithm for this problem.
AB - We consider the following 2-clustering problem. Given N points in Euclidean space, partition it into two subsets (clusters) so that the sum of squared distances between the elements of the clusters and their centers would be minimum. The center of the first cluster coincides with its centroid (mean) while the center of the second cluster should be chosen from the set of the initial points (medoid). It is known that this problem is NP-hard if the cardinalities of the clusters are given as a part of the input. In this paper we prove that the problem remains NP-hard in the case of arbitrary clusters sizes and suggest a 2-approximation polynomial-time algorithm for this problem.
KW - 2-approximation algorithm
KW - 2-clustering
KW - Euclidean space
KW - mean
KW - medoid
KW - strong NP-hardness
UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85149693690&origin=inward&txGid=0ef835b184192143eff47bbc12190b22
UR - https://www.mendeley.com/catalogue/adffbbb5-d90a-3dda-9c53-da152eb16724/
U2 - 10.2298/YJOR211018008P
DO - 10.2298/YJOR211018008P
M3 - Article
VL - 33
SP - 59
EP - 69
JO - Yugoslav Journal of Operations Research
JF - Yugoslav Journal of Operations Research
SN - 0354-0243
IS - 1
ER -
ID: 56398412