Approximation algorithms for stochastic clustering

被引：0

作者：

Harris, David G. ^{[1
]}

Li, Shi ^{[2
]}

Pensyl, Thomas ^{[3
]}

Srinivasan, Aravind ^{[1
,4
]}

Khoa Trinh ^{[5
]}

机构：

[1] Univ Maryland, Dept Comp Sci, College Pk, MD 20742 USA

[2] SUNY Buffalo, Buffalo, NY USA

[3] Bandwidth Inc, Raleigh, NC USA

[4] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA

[5] Google, Mountain View, CA 94043 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018) | 2018年 / 31卷

关键词：

clustering; k-center; k-median; lottery; approximation algorithms; RACE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider stochastic settings for clustering, and develop provably-good (approximation) algorithms for a number of these notions. These algorithms allow one to obtain better approximation ratios compared to the usual deterministic clustering setting. Additionally, they offer a number of advantages including providing fairer clustering and clustering which has better long-term behavior for each user. In particular, they ensure that every user is guaranteed to get good service (on average). We also complement some of these with impossibility results.

引用

页数：10

共 50 条

[1] Approximation algorithms for stochastic clustering
Harris, David G.
Li, Shi
Pensyl, Thomas
Srinivasan, Aravind
Trinh, Khoa
[J]. JOURNAL OF MACHINE LEARNING RESEARCH, 2019, 20
[2] Approximation algorithms for stochastic clustering
Harris, David G.
Li, Shi
Pensyl, Thomas
Srinivasan, Aravind
Trinh, Khoa
[J]. Journal of Machine Learning Research, 2019, 20
[3] Exact and approximation algorithms for clustering
Agarwal, PK
Procopiuc, CM
[J]. ALGORITHMICA, 2002, 33 (02) : 201 - 226
[4] Approximation algorithms for projective clustering
Agarwal, PK
Procopiuc, CM
[J]. JOURNAL OF ALGORITHMS-COGNITION INFORMATICS AND LOGIC, 2003, 46 (02): : 115 - 139
[5] Approximation Algorithms for Tensor Clustering
Jegelka, Stefanie
Sra, Suvrit
Banerjee, Arindam
[J]. ALGORITHMIC LEARNING THEORY, PROCEEDINGS, 2009, 5809 : 368 - +
[6] Exact and Approximation Algorithms for Clustering
P. K. Agarwal
C. M. Procopiuc
[J]. Algorithmica, 2002, 33 : 201 - 226
[7] Randomized Stochastic Approximation Algorithms
Amelin, Konstantin
Granichin, Oleg
Granichina, Olga
[J]. 2014 EUROPEAN CONTROL CONFERENCE (ECC), 2014, : 2827 - 2832
[8] Dynamics of stochastic approximation algorithms
Benaïm, M
[J]. SEMINAIRE DE PROBABILITES XXXIII, 1999, 1709 : 1 - 68
[9] Are approximation algorithms for consensus clustering worthwhile?
Bertolacci, Michael
Wirth, Anthony
[J]. PROCEEDINGS OF THE SEVENTH SIAM INTERNATIONAL CONFERENCE ON DATA MINING, 2007, : 437 - 442
[10] On parameterized approximation algorithms for balanced clustering
Xiangyan Kong
Zhen Zhang
Qilong Feng
[J]. Journal of Combinatorial Optimization, 2023, 45

← 1 2 3 4 5 →