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k-means++: few more steps yield constant approximation

k-means++: few more steps yield constant approximation

International Conference on Machine Learning (ICML), 2020
18 February 2020
Davin Choo
Christoph Grunau
Julian Portmann
Václav Rozhon
ArXiv (abs)PDFHTML

Papers citing "k-means++: few more steps yield constant approximation"

16 / 16 papers shown
TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation
TTS-VAR: A Test-Time Scaling Framework for Visual Auto-Regressive Generation
Zhekai Chen
Ruihang Chu
Yukang Chen
Shiwei Zhang
Yujie Wei
Yingya Zhang
Xihui Liu
297
10
0
24 Jul 2025
LEANN: A Low-Storage Vector Index
LEANN: A Low-Storage Vector Index
Yichuan Wang
Zhifei Li
Shu Liu
Yongji Wu
Ron Yifeng Wang
...
Yang Zhou
Eric Liang
Sewon Min
Matei A. Zaharia
Joseph E. Gonzalez
362
2
0
09 Jun 2025
A statistically consistent measure of semantic uncertainty using Language Models
A statistically consistent measure of semantic uncertainty using Language Models
Yi Liu
373
0
0
01 Feb 2025
A3S: A General Active Clustering Method with Pairwise Constraints
A3S: A General Active Clustering Method with Pairwise Constraints
Xun Deng
Junlong Liu
Han Zhong
Fuli Feng
Chen Shen
Xiangnan He
Jieping Ye
Zheng Wang
175
4
0
14 Jul 2024
Hierarchical Clustering via Local Search
Hierarchical Clustering via Local Search
Hossein Jowhari
86
1
0
24 May 2024
A Scalable Algorithm for Individually Fair K-means Clustering
A Scalable Algorithm for Individually Fair K-means ClusteringInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024
M. Bateni
Vincent Cohen-Addad
Alessandro Epasto
Silvio Lattanzi
355
12
0
09 Feb 2024
Simple, Scalable and Effective Clustering via One-Dimensional
  Projections
Simple, Scalable and Effective Clustering via One-Dimensional ProjectionsNeural Information Processing Systems (NeurIPS), 2023
Moses Charikar
Monika Henzinger
Lunjia Hu
Maximilian Vötsch
Erik Waingarten
251
3
0
25 Oct 2023
An Analysis of $D^α$ seeding for $k$-means
An Analysis of DαD^αDα seeding for kkk-means
Étienne Bamas
Sai Ganesh Nagarajan
Ola Svensson
144
0
0
20 Oct 2023
Global $k$-means$++$: an effective relaxation of the global $k$-means
  clustering algorithm
Global kkk-means++++++: an effective relaxation of the global kkk-means clustering algorithm
Georgios Vardakas
A. Likas
274
40
0
22 Nov 2022
A Nearly Tight Analysis of Greedy k-means++
A Nearly Tight Analysis of Greedy k-means++ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022
Christoph Grunau
Ahmet Alper Ozudougru
Václav Rozhon
Jakub Tvetek
181
15
0
16 Jul 2022
$k$-Median Clustering via Metric Embedding: Towards Better
  Initialization with Differential Privacy
kkk-Median Clustering via Metric Embedding: Towards Better Initialization with Differential PrivacyNeural Information Processing Systems (NeurIPS), 2022
Chenglin Fan
Ping Li
Xiaoyun Li
364
8
0
26 Jun 2022
Adaptive Methods for Aggregated Domain Generalization
Adaptive Methods for Aggregated Domain Generalization
Xavier Thomas
D. Mahajan
A. Pentland
Abhimanyu Dubey
OOD
186
10
0
09 Dec 2021
Improved Guarantees for k-means++ and k-means++ Parallel
Improved Guarantees for k-means++ and k-means++ ParallelNeural Information Processing Systems (NeurIPS), 2020
K. Makarychev
Aravind Reddy
Liren Shan
DRL
296
28
0
27 Oct 2020
Too Much Information Kills Information: A Clustering Perspective
Too Much Information Kills Information: A Clustering Perspective
Yicheng Xu
Vincent Chau
Chenchen Wu
Yong Zhang
V. Zissimopoulos
Yifei Zou
DRL
61
1
0
16 Sep 2020
Adapting $k$-means algorithms for outliers
Adapting kkk-means algorithms for outliers
Christoph Grunau
Václav Rozhon
271
7
0
02 Jul 2020
Noisy, Greedy and Not So Greedy k-means++
Noisy, Greedy and Not So Greedy k-means++Embedded Systems and Applications (ESA), 2019
Anup Bhattacharya
Jan Eube
Heiko Röglin
Melanie Schmidt
207
17
0
02 Dec 2019
1
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