Adaptive clustering uses kernel density estimates for split detection.
problem Cluster detection in non-parametric settings.
method Recursive algorithm using kernel density estimates for splitting and clustering.
result Finite sample guarantees, consistency, and adaptive bandwidth selection.
New method estimates number of clusters robustly in noisy data.
problem Challenges in estimating number of clusters in noisy data.
method Robust Bayesian cluster enumeration using t distribution. result Proposes a two-step algorithm for robust cluster enumeration.
Study connects spectral clustering to maximum margin and level set estimation.
problem Connecting spectral clustering to maximum margin and level set estimation.
method Obtained bounds on eigenvectors of graph Laplacian matrices in terms of cluster separation and connectivity. Showed sensitivity mitigation by removing outliers and estimating level sets.
result Spectral clustering converges to maximum margin clustering as scaling parameter approaches zero.
Efficient clustering in high dimensions with Quick Shift and LSH.
problem Density-based clustering in high-dimensional data.
method Combines Quick Shift and LSH for efficient density estimation.
result Achieves almost linear time complexity for consistency.
Clustering stocks reduces estimation error in global minimum variance portfolio.
problem High estimation error in covariance matrix estimation.
method Bounded clustering to limit maximum cluster size.
result Reduction in out-of-sample volatility and gap between in-sample and out-of-sample volatility.
Parameter-free clustering method using cluster catch digraphs (CCDs).
problem Finding the correct number of clusters in data without specifying a parameter.
method Hybrid of density-based and graph-based clustering methods using Ripley's K function.
result Minimum dominating sets of RK-CCDs estimate and distinguish clusters from noise.
BCV method helps estimate clusters and hyper-parameters in large data sets.
problem Determining the number of clusters in large-scale data.
method Bi-cross validation (BCV) for spectral clustering.
result BCV directly applies to spectral clustering for estimating clusters and hyper-parameters.
Entropy regularization improves interpretability of probabilistic clustering models.
problem Bayesian nonparametric mixture models often produce unbalanced cluster frequencies.
method Interpreting the posterior as penalized likelihood, entropy regularization reduces sparsely-populated clusters.
result The proposed entropy-regularized estimator enhances interpretability without sacrificing computational convenience.
A new clustering method uses nonparametric smoothing to estimate cluster membership functions.
problem Clustering with flexible, nonparametric estimation.
method Nonparametric smoothing to estimate cluster membership functions without explicit modelling assumptions.
result The method automatically determines the number of clusters and level of flexibility.
In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being ro…
DISCERN estimates K for K-Means clustering efficiently.
problem Estimating the number of clusters in data.
method A deterministic initialization method for K-Means++.
result Improves both K estimation and clustering performance.
JojoSCL improves scRNA-seq clustering by reducing intra-cluster dispersion.
problem High dimensionality and sparsity of scRNA-seq data challenge clustering models.
method Integrates shrinkage estimator and contrastive learning for improved clustering.
result JojoSCL outperforms existing methods on ten scRNA-seq datasets.
A new method estimates the number of clusters on spherical data.
problem Estimating the number of clusters in spherical data.
method Spherical X-means (SX-means) method assuming von Mises-Fisher distributions.
result Shows the performance of SX-means in estimating the number of clusters.
Mean shift clustering finds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to fix the number of clusters in advance, the mean shift has been a popular clustering algorithm in various application fields. A typical implementation of the mean shi…
Most density-based clustering methods largely rely on how well the underlying density is estimated. However, density estimation itself is also a challenging problem, especially the determination of the kernel bandwidth. A large bandwidth could lead to the over-smoothed density estimation in which the number of density …
A new clustering evaluation index based on density estimation.
problem Improving internal clustering evaluation indices.
method The index is a mixture of Ambiguous and Similarity sub-indices, calculated using density estimation.
result The new index significantly outperforms other internal clustering evaluation indices.
A new frequency-based index estimates sub-cluster connections.
problem Determining the strength of connections between similar sub-clusters.
method Frequency coefficient based on the SST poverty index for clustering.
result Estimation of connection strength reveals natural homogeneous clusters.
New method for clustering tasks with heterogeneous data.
problem Clustered multitask learning with semiparametric and heterogeneous nuisances.
method Adaptive fused orthogonal estimator with Neyman-orthogonal losses and data-driven fusion penalties.
result Achieves exact clustering recovery and pooled parametric convergence rates.
Two new methods improve clustering with missing data.
problem Handling missing data in Gaussian Mixture Models.
method Proposes two methods using Monte Carlo Expectation-Maximization (MCEM) for data augmentation.
result Proposed methods outperform multiple imputation in clustering and density estimation.
This paper improves robust cluster enumeration for RES data.
problem Challenges in determining optimal clusters in noisy data.
method Generalizes robust Bayesian cluster enumeration for RES mixtures.
result Significant robustness improvement over existing methods.
New method estimates density-derivative-ratios directly for clustering and ridge estimation.
problem Accurately estimating ratios of density derivatives.
method Direct estimation of density-derivative-ratios without density estimation.
result Developed methods significantly outperform existing techniques, especially for high-dimensional data.
OSil algorithm optimizes clustering quality using ASW.
problem Optimizing clustering quality using ASW.
method Distance-based objective function optimizing ASW for clustering.
result OSil algorithm outperforms other clustering methods in clustering quality.
The paper reformulates clustering as matrix factorization on the Stiefel manifold.
problem Clustering high-dimensional data like images and gene expression.
method Reformulates clustering as low-rank matrix estimation, using Burer-Monteiro factorization on the Stiefel manifold.
result Proves novel prediction bounds for clustering and proposes a componentwise Langevin sampler.
New estimator uses clustering to improve off-policy evaluation accuracy.
problem Improving off-policy evaluation accuracy when logging and evaluation policies differ.
method Proposes an estimator that shares information across similar contexts using clustering.
result Clustering contexts improves estimation accuracy, especially in deficient information settings.
For a density f on Rd, a {\it high-density cluster} is any connected component of {x:f(x)≥λ}, for some λ>0. The set of all high-density clusters forms a hierarchy called the {\it cluster tree} of f. We present two procedures for estimating the cluster tree given samples from f. The first…
CDL index improves clustering validation for non-convex data.
problem Selecting clustering algorithms and hyperparameters without labeled data.
method CDL uses compactness, centers, and covariances to compute a probabilistic description length bound.
result CDL outperforms conventional CVIs on synthetic and image benchmarks.
Paper proposes a method to estimate cluster number without prior knowledge.
problem Community detection in unlabeled networks.
method Semi-definite relaxations for estimating cluster number and matrix.
result Method recovers cluster number and matrix exactly with high probability.
Paper introduces new cluster-based graphical models for high-dimensional data.
problem Inference for high-dimensional graphical models with many features.
method Cluster-based model with model-assisted clustering; likelihood-based estimation and inference strategies.
result Developed estimators for precision matrix of latent vector, with asymptotic central limit theorems.
Develops a provable convex tensor clustering method.
problem Cluster analysis of tensors, especially in high dimensions.
method Provably convex formulation of tensor co-clustering.
result Non-asymptotic error bound revealing 'blessing of dimensionality'.
New RESK distributions improve robust clustering of skewed data.
problem Robustly clustering non-symmetric, heavy-tailed data clusters.
method Proposes RESK distributions and an EM algorithm with robust skew-Huber M-estimator.
result Numerical experiments confirm the effectiveness of the proposed methods.
Proposes a robust clustering method using the Median-of-Means estimator.
problem Noise and outliers in data affect clustering quality and require specifying the number of clusters.
method Integrates model-based and centroid-based clustering methods using the Median-of-Means estimator.
result Mitigates noise effects and estimates the number of clusters automatically.
Gradient Boosted Mixed Models estimate mean and variance components for clustered data.
problem Limited flexibility in linear mixed models for complex settings.
method Gradient Boosting extended to mixed models with likelihood-based gradients and flexible base learners.
result Accurate recovery of variance components and improved predictive accuracy.
In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input space, representative of data in their cluster. This is challenging with data having a nonconvex or manifold structure, as with images or text…
The paper proposes a new method for density estimation using spline quasi-interpolation for clustering.
problem Density estimation and clustering modeling for multivariate data.
method Spline quasi-interpolation for mono-variate approximation, copulas for multivariate modeling.
result The proposed method achieves accurate clustering of data using copulas and spline quasi-interpolation.
Parallel neural networks estimate TVD for merging over-clustered datasets.
problem Merging over-partitioned clusters in unsupervised learning.
method Use neural networks to estimate TVD between clusters in parallel.
result Neural network estimates of TVD lead to better merge decisions.
Syncytial clustering merges groups from standard algorithms to reveal complex data structures.
problem Challenges in finding clusters with irregular structures.
method Estimates nonparametric overlap between clusters and merges groups with high overlap.
result Always a top performer in identifying groups with regular and irregular structures.
New visual quality index for fuzzy clustering.
problem No accurate quality index for fuzzy clustering across datasets.
method Proposes a new visual quality index and graph-based solution.
result Validated through extensive experiments on various datasets.
Method estimates number of clusters in Block Markov Chain trajectories.
problem Challenges in choosing number of clusters for sequential data.
method Spectral embedding and density-based clustering.
result Asymptotically consistent method for estimating clusters.
Proposes a weighted conformal approach for cluster label uncertainty.
problem Cluster label uncertainty in unlabeled data.
method Develops a conformal inference algorithm to correct label mismatch.
result Improves confidence set size in nonlinear and high-dimensional clustering.
MTLRRC improves MTL by robustly clustering tasks and detecting outliers.
problem Improving MTL by handling outlier tasks and sharing common information.
method Robust regularized clustering with non-convex group penalties.
result MTLRRC effectively detects and clusters tasks, improving overall performance.
Discussing issues in robust clustering, especially with Gaussian models.
problem Handling outliers and ambiguity in clustering groups.
method Focus on Gaussian mixture model, examining formal definitions, interactions, and tuning decisions.
result Outliers can confuse clustering groups and existing stability measures fail with them.
New clustering algorithm uses reverse nearest neighbour for better density-based clustering.
problem Density-based clustering of separated high-density regions.
method Uses reverse nearest neighbour (RNN) queries to estimate densities and recover clusters.
result Outperforms DBSCAN and ISDBSCAN on synthetic and real-world data.
The paper addresses Qini curve estimation under clustered network interference.
problem Qini curves can be biased when interference is ignored in clustered network settings.
method Proposes three estimation strategies for clustered network interference.
result Identifies the most appropriate approach based on bias-variance trade-offs.
Develops a new random forest method for clustered data with improved prediction and inference.
problem Improving prediction and inference accuracy for clustered data with within-cluster dependence.
method Clustered Random Forests, using weighted least squares estimators for leaf predictions.
result Optimal prediction and inference weights vary under covariate shift, necessitating user-chosen weights.
The study optimizes bandwidth for nonparametric modal clustering.
problem Optimizing bandwidth for nonparametric modal clustering.
method Asymptotic analysis of density-based partitions and bandwidth selection.
result Asymptotic approximation of a metric for partition distance.
High density clusters can be characterized by the connected components of a level set L(λ)={x: p(x)>λ} of the underlying probability density function p generating the data, at some appropriate level λ≥0. The complete hierarchical clustering can be characterized by a cluster tree ${\cal T}= \bigcup_λ L(λ)…
t-NEB clusters high-dimensional data hierarchically with density paths.
problem Hierarchical clustering struggles with high-dimensional data.
method t-NEB uses density estimation, maximum density paths, and probabilistic merging.
result t-NEB yields state-of-the-art clustering performance on high-dimensional data.
A new framework for clustering with uncertainty quantification.
problem Lack of uncertainty quantification in clustering methods.
method Generalized Bayes framework using Gibbs posteriors and loss functions.
result Efficient algorithms for clustering and uncertainty quantification.