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Clustering Stability: An Overview

Clustering Stability: An Overview

7 July 2010
U. V. Luxburg
ArXiv (abs)PDFHTML

Papers citing "Clustering Stability: An Overview"

49 / 49 papers shown
Designing Rules to Pick a Rule: Aggregation by Consistency
Designing Rules to Pick a Rule: Aggregation by Consistency
Ratip Emin Berker
Ben Armstrong
Vincent Conitzer
Nihar B. Shah
68
0
0
24 Aug 2025
On uniqueness of the set of k-means
On uniqueness of the set of k-means
Javier Cárcamo
Antonio Cuevas
Luis-Alberto Rodríguez
137
0
0
17 Oct 2024
SHADE: Deep Density-based Clustering
SHADE: Deep Density-based ClusteringIndustrial Conference on Data Mining (IDM), 2024
Anna Beer
Pascal Weber
Lukas Miklautz
Collin Leiber
Walid Durani
Christian Böhm
Claudia Plant
125
1
0
08 Oct 2024
Interpretable Clustering with the Distinguishability Criterion
Interpretable Clustering with the Distinguishability Criterion
Ali Turfah
Xiaoquan Wen
248
0
0
24 Apr 2024
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
Towards Generalized Multi-stage Clustering: Multi-view Self-distillation
Jiatai Wang
Zhiwei Xu
Xin Wang
Tao Li
249
1
0
29 Oct 2023
Model-based Clustering of Individuals' Ecological Momentary Assessment
  Time-series Data for Improving Forecasting Performance
Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance
Mandani Ntekouli
Gerasimos Spanakis
L. Waldorp
A. Roefs
135
1
0
11 Oct 2023
Replicable Clustering
Replicable ClusteringNeural Information Processing Systems (NeurIPS), 2023
Hossein Esfandiari
Amin Karbasi
Vahab Mirrokni
Grigoris Velegkas
Felix Y. Zhou
380
14
0
20 Feb 2023
Clustering individuals based on multivariate EMA time-series data
Clustering individuals based on multivariate EMA time-series data
Mandani Ntekouli
Gerasimos Spanakis
L. Waldorp
A. Roefs
83
2
0
02 Dec 2022
How To Overcome Richness Axiom Fallacy
How To Overcome Richness Axiom Fallacy
M. Klopotek
R. A. Klopotek
128
0
0
27 Oct 2022
AICCA: AI-driven Cloud Classification Atlas
AICCA: AI-driven Cloud Classification AtlasRemote Sensing (RS), 2022
Takuya Kurihana
Elisabeth Moyer
Ian Foster
260
16
0
29 Sep 2022
Sanity Check for External Clustering Validation Benchmarks using
  Internal Validation Measures
Sanity Check for External Clustering Validation Benchmarks using Internal Validation Measures
Hyeon Jeon
Michaël Aupetit
DongHwa Shin
Aeri Cho
Seokhyeon Park
Jinwook Seo
143
5
0
20 Sep 2022
Attributed Network Embedding Model for Exposing COVID-19 Spread
  Trajectory Archetypes
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory ArchetypesInternational Journal of Data Science and Analysis (JDSA), 2022
Junwei Ma
Bo Li
Qingchun Li
Chao Fan
Ali Mostafavi
248
3
0
20 Sep 2022
Survival Kernets: Scalable and Interpretable Deep Kernel Survival
  Analysis with an Accuracy Guarantee
Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy GuaranteeJournal of machine learning research (JMLR), 2022
George H. Chen
749
5
0
21 Jun 2022
Response to: Significance and stability of deep learning-based
  identification of subtypes within major psychiatric disorders. Molecular
  Psychiatry (2022)
Response to: Significance and stability of deep learning-based identification of subtypes within major psychiatric disorders. Molecular Psychiatry (2022)Molecular Psychiatry (Mol Psychiatry), 2022
Xizhe Zhang
Haiwei Yang
Weixiong Zhang
26
4
0
10 Jun 2022
Reproducibility in Learning
Reproducibility in LearningSymposium on the Theory of Computing (STOC), 2022
R. Impagliazzo
Rex Lei
T. Pitassi
Jessica Sorrell
294
60
0
20 Jan 2022
Self-Supervised Representation Learning: Introduction, Advances and
  Challenges
Self-Supervised Representation Learning: Introduction, Advances and Challenges
Linus Ericsson
Henry Gouk
Chen Change Loy
Timothy M. Hospedales
SSLOODAI4TS
304
355
0
18 Oct 2021
Row-clustering of a Point Process-valued Matrix
Row-clustering of a Point Process-valued Matrix
Lihao Yin
Ganggang Xu
H. Sang
Yongtao Guan
177
6
0
04 Oct 2021
Adaptive unsupervised learning with enhanced feature representation for
  intra-tumor partitioning and survival prediction for glioblastoma
Adaptive unsupervised learning with enhanced feature representation for intra-tumor partitioning and survival prediction for glioblastoma
Yifan Li
Chao Li
Yiran Wei
S. Price
Carola-Bibiane Schönlieb
Xi Chen
32
1
0
21 Aug 2021
Zooming Into the Darknet: Characterizing Internet Background Radiation
  and its Structural Changes
Zooming Into the Darknet: Characterizing Internet Background Radiation and its Structural Changes
Michalis Kallitsis
Vasant Honavar
Rupesh Prajapati
Dinghao Wu
J. Yen
35
7
0
29 Jul 2021
Estimation of the Global Mode of a Density: Minimaxity, Adaptation, and
  Computational Complexity
Estimation of the Global Mode of a Density: Minimaxity, Adaptation, and Computational ComplexityElectronic Journal of Statistics (EJS), 2021
E. Arias-Castro
Wanli Qiao
Lin Zheng
126
5
0
16 Apr 2021
Data-driven Cloud Clustering via a Rotationally Invariant Autoencoder
Data-driven Cloud Clustering via a Rotationally Invariant AutoencoderIEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
Takuya Kurihana
Elisabeth Moyer
Rebecca Willett
Davis Gilton
Ian Foster
136
19
0
08 Mar 2021
Bayesian optimization assisted unsupervised learning for efficient intra-tumor partitioning in MRI and survival prediction for glioblastoma patients
Yifan Li
Chao Li
S. Price
Carola-Bibiane Schönlieb
Xi Chen
88
0
0
05 Dec 2020
How Far Does BERT Look At:Distance-based Clustering and Analysis of
  BERT$'$s Attention
How Far Does BERT Look At:Distance-based Clustering and Analysis of BERT′'′s AttentionInternational Conference on Computational Linguistics (COLING), 2020
Yue Guan
Jingwen Leng
Chao Li
Quan Chen
Minyi Guo
176
19
0
02 Nov 2020
Visually Analyzing Contextualized Embeddings
Visually Analyzing Contextualized EmbeddingsVisual .. (VISUAL), 2020
M. Berger
126
15
0
05 Sep 2020
reval: a Python package to determine best clustering solutions with
  stability-based relative clustering validation
reval: a Python package to determine best clustering solutions with stability-based relative clustering validation
Isotta Landi
Veronica Mandelli
M. Lombardo
52
2
0
27 Aug 2020
Selecting the Number of Clusters $K$ with a Stability Trade-off: an
  Internal Validation Criterion
Selecting the Number of Clusters KKK with a Stability Trade-off: an Internal Validation Criterion
Alex Mourer
Florent Forest
M. Lebbah
Hanane Azzag
J. Lacaille
286
8
0
15 Jun 2020
A Unified Framework for Tuning Hyperparameters in Clustering Problems
A Unified Framework for Tuning Hyperparameters in Clustering ProblemsStatistica sinica (Stat. Sinica), 2019
Xinjie Fan
Yuguang Yue
Purnamrita Sarkar
Y. X. R. Wang
270
2
0
17 Oct 2019
Bi-cross validation for estimating spectral clustering hyper parameters
Bi-cross validation for estimating spectral clustering hyper parametersPowder Diffraction (PD), 2019
S. Zohar
C. Yoon
129
0
0
10 Aug 2019
A close-up comparison of the misclassification error distance and the
  adjusted Rand index for external clustering evaluation
A close-up comparison of the misclassification error distance and the adjusted Rand index for external clustering evaluationBritish Journal of Mathematical & Statistical Psychology (BJMSP), 2019
José E. Chacón
189
18
0
26 Jul 2019
A numerical measure of the instability of Mapper-type algorithms
A numerical measure of the instability of Mapper-type algorithmsJournal of machine learning research (JMLR), 2019
Francisco Belchí Guillamón
J. Brodzki
Matthew I. Burfitt
M. Niranjan
74
17
0
04 Jun 2019
Modal clustering asymptotics with applications to bandwidth selection
Modal clustering asymptotics with applications to bandwidth selection
A. Casa
José E. Chacón
G. Menardi
128
9
0
22 Jan 2019
Is Data Clustering in Adversarial Settings Secure?
Is Data Clustering in Adversarial Settings Secure?Security and Artificial Intelligence (SAI), 2013
Battista Biggio
I. Pillai
Samuel Rota Buló
Andrea Valenza
Marcello Pelillo
Fabio Roli
AAML
184
134
0
25 Nov 2018
$k$-means as a variational EM approximation of Gaussian mixture models
kkk-means as a variational EM approximation of Gaussian mixture models
Jörg Lücke
D. Forster
DRLVLM
403
52
0
16 Apr 2017
On the Discrepancy Between Kleinberg's Clustering Axioms and $k$-Means
  Clustering Algorithm Behavior
On the Discrepancy Between Kleinberg's Clustering Axioms and kkk-Means Clustering Algorithm Behavior
R. A. Klopotek
M. Klopotek
80
9
0
15 Feb 2017
Clustering with Confidence: Finding Clusters with Statistical Guarantees
Clustering with Confidence: Finding Clusters with Statistical Guarantees
A. Henelius
Kai Puolamäki
Henrik Bostrom
P. Papapetrou
OOD
99
4
0
27 Dec 2016
Likelihood Inference for Large Scale Stochastic Blockmodels with
  Covariates based on a Divide-and-Conquer Parallelizable Algorithm with
  Communication
Likelihood Inference for Large Scale Stochastic Blockmodels with Covariates based on a Divide-and-Conquer Parallelizable Algorithm with Communication
S. Roy
Yves Atchadé
George Michailidis
196
17
0
30 Oct 2016
Convergence rate of stochastic k-means
Convergence rate of stochastic k-means
Cheng Tang
C. Monteleoni
206
26
0
16 Oct 2016
Constraint-Based Clustering Selection
Constraint-Based Clustering Selection
Toon van Craenendonck
Hendrik Blockeel
212
33
0
23 Sep 2016
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised
  Algorithm
Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised AlgorithmJournal of Artificial Intelligence Research (JAIR), 2016
Yury Maximov
Massih-Reza Amini
Zaïd Harchaoui
299
35
0
02 Jul 2016
The Mean Partition Theorem of Consensus Clustering
The Mean Partition Theorem of Consensus Clustering
Brijnesh J. Jain
137
6
0
22 Apr 2016
Homogeneity of Cluster Ensembles
Homogeneity of Cluster Ensembles
Brijnesh J. Jain
119
5
0
08 Feb 2016
k-variates++: more pluses in the k-means++
k-variates++: more pluses in the k-means++
Richard Nock
Raphaël Canyasse
R. Boreli
Frank Nielsen
DRL
242
23
0
03 Feb 2016
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
Multicuts and Perturb & MAP for Probabilistic Graph Clustering
Jörg H. Kappes
Paul Swoboda
Bogdan Savchynskyy
Tamir Hazan
Christoph Schnörr
197
7
0
09 Jan 2016
Validation of neural spike sorting algorithms without ground-truth
  information
Validation of neural spike sorting algorithms without ground-truth informationJournal of Neuroscience Methods (J. Neurosci. Methods), 2015
A. Barnett
J. Magland
L. Greengard
88
49
0
27 Aug 2015
Notes on using Determinantal Point Processes for Clustering with
  Applications to Text Clustering
Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
Apoorv Agarwal
A. Choromańska
K. Choromanski
224
5
0
26 Oct 2014
Splitting Methods for Convex Clustering
Splitting Methods for Convex ClusteringJournal of Computational And Graphical Statistics (JCGS), 2013
Eric C. Chi
K. Lange
425
281
0
01 Apr 2013
Improved Cheeger's Inequality: Analysis of Spectral Partitioning
  Algorithms through Higher Order Spectral Gap
Improved Cheeger's Inequality: Analysis of Spectral Partitioning Algorithms through Higher Order Spectral GapSymposium on the Theory of Computing (STOC), 2013
T. C. Kwok
L. Lau
Y. Lee
S. Gharan
Luca Trevisan
700
103
0
23 Jan 2013
A Kernel Multiple Change-point Algorithm via Model Selection
A Kernel Multiple Change-point Algorithm via Model Selection
Sylvain Arlot
Alain Celisse
Zaïd Harchaoui
395
44
0
17 Feb 2012
Stability of Density-Based Clustering
Stability of Density-Based Clustering
Alessandro Rinaldo
Aarti Singh
Rebecca Nugent
Larry A. Wasserman
182
69
0
11 Nov 2010
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