Proposes a new model for unsupervised clustering with latent variables.
problem The challenge of unsupervised clustering in machine learning.
method Clustered Generator Model with continuous and discrete latent variables.
result Achieves competitive unsupervised clustering accuracy and disentangled latent representations.
In this paper, we propose a general model for plane-based clustering. The general model contains many existing plane-based clustering methods, e.g., k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector clustering (TWSVC) and its extensions. Under this general model, one may obtain an appropria…
This study evaluates cluster search algorithms using Gaussian mixture models.
problem Determining the optimal number of clusters in data sets generated by Gaussian mixture models.
method Examined centroid- and model-based cluster search algorithms in various cases.
result Model-based algorithms are more robust to cluster overlap and covariance type than centroid-based methods.
Integrates VAEs into EM for deep clustering and generation.
problem Clustering and generating new samples from complex distributions.
method Combines VAEs and EM, updating model parameters and refining cluster assignments.
result Superior clustering performance on MNIST and FashionMNIST.
ARMED models improve deep learning interpretability and generalize better on clustered data.
problem Clustered data leads to spurious associations and poor model fitting.
method Adversarial regularization and mixed effects subnetworks.
result ARMED models outperform conventional methods in accuracy and generalization.
The paper introduces the concept of a cluster structure to define a joint distribution of the sample size and its exchangeable random partitions. The cluster structure allows the probability distribution of the random partitions of a subset of the sample to be dependent on the sample size, a feature not presented in a …
Adaptive graph auto-encoder improves general data clustering.
problem Extending graph convolution networks to general clustering tasks.
method Adaptive graph construction based on generative perspective, novel decoder design.
result Model performs well in weighted graph scenarios.
Meta-learning model clusters data better than standard methods.
problem Challenges in clustering due to custom loss functions and small datasets.
method Trains a recurrent model to learn clustering from various datasets.
result Meta clustering model outperforms standard clustering techniques on unseen datasets.
K-ARMA models cluster time series data robustly.
problem Clustering time series data effectively.
method Model-based K-ARMA clustering algorithm with robust outlier detection.
result K-ARMA models outperform existing methods for time series clustering.
The learning of mixture models can be viewed as a clustering problem. Indeed, given data samples independently generated from a mixture of distributions, we often would like to find the {\it correct target clustering} of the samples according to which component distribution they were generated from. For a clustering pr…
A new method clusters survival data using deep variational models.
problem Clustering survival data, a challenging task.
method Semi-supervised probabilistic approach with deep generative model.
result The method outperforms previous models in clustering and predicting survival times.
We describe a probabilistic (generative) view of affinity matrices along with inference algorithms for a subclass of problems associated with data clustering. This probabilistic view is helpful in understanding different models and algorithms that are based on affinity functions OF the data. IN particular, we show how(…
Paper presents robust clustering methods for general mixture models.
problem Clustering with sub-Gaussian error assumptions often invalid in practice.
method Hybrid clustering with robust centroid estimate and data-driven initialization.
result Provably near-optimal mislabeling guarantees for general error distributions.
clusterBMA combines clustering results from multiple models using Bayesian model averaging.
problem Uncertainty in model selection for clustering.
method Bayesian model averaging to combine results from multiple clustering algorithms.
result ClusterBMA offers probabilistic cluster allocations and quantifies model-based uncertainty.
A new framework for predictive clustering and optimization.
problem Finding clusters of data that yield low error on a supervised target.
method Generalized optimization framework using MILP and MM for scalability.
result Models can uncover different interpretable discrete cluster structures.
Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not f…
Graph clustering involves the task of dividing nodes into clusters, so that the edge density is higher within clusters as opposed to across clusters. A natural, classic and popular statistical setting for evaluating solutions to this problem is the stochastic block model, also referred to as the planted partition model…
New deep learning framework for tabular data clusters with interpretable features.
problem Need for reliable and interpretable clustering models for tabular data.
method Self-supervised feature selection and gate matrix for cluster-level feature selection.
result Model provides interpretable cluster assignments with driving features.
Model-based clustering defines population level clusters relative to a model that embeds notions of similarity. Algorithms tailored to such models yield estimated clusters with a clear statistical interpretation. We take this view here and introduce the class of G-block covariance models as a background model for varia…
End-to-end clustering without labels using normalized cuts.
problem Clustering unlabeled data without supervision.
method Learning to optimize expected normalized cuts, differentiable loss function.
result State-of-the-art results on various benchmarks, generalizing to new datasets.
GMMSEQ clusters AE data streams, identifying cluster onsets and growth.
problem Clustering unlabeled AE data with onsets and growth information.
method Modified Gaussian Mixture Models (GMM) with an objective function for AE timestamps.
result GMMSEQ provides better cluster characterization than standard methods.
CURE extracts relations without supervision by clustering similar entity pairs.
problem Extracting relations unsupervised without considering sentence correlations.
method CURE uses Encoder-Decoder architecture for self-supervised learning and clustering similar relations.
result CURE outperforms state-of-the-art models on NYT and UNPC datasets.
The paper proposes a method to select clusters, models, and algorithms based on quadratic discriminant scores.
problem Selecting the number of clusters, models, and algorithms in cluster analysis.
method Develops quadratic scores for cluster quality, uses bootstrap resampling, and compares partitions.
result The proposed method achieves better overall performance compared to other state-of-the-art methods.
The paper analyzes how clustering sensitive data can improve model generalization without revealing individual information.
problem Ensuring user data privacy in personalized recommendation systems.
method Look-alike clustering to replace sensitive features with cluster averages, analyzed using Convex Gaussian Minimax Theorem.
result Training models using anonymous cluster centers can improve generalization error, especially in high-dimensional settings.
In the context of clustering, we assume a generative model where each cluster is the result of sampling points in the neighborhood of an embedded smooth surface; the sample may be contaminated with outliers, which are modeled as points sampled in space away from the clusters. We consider a prototype for a higher-order …
A new clustering model for mixed datasets combines continuous and non-continuous data.
problem Challenges in clustering mixed data due to heterogeneous variables.
method Mixed Deep Gaussian Mixture Model (MDGMM) with multilayer architecture.
result Automatic selection of model specifications and optimal number of clusters.
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable cluste…
Posterior regularization enhances Bayesian hierarchical mixture clustering by improving node separation.
problem High nodal variance in BHMC trees, leading to weak separation between nodes at higher levels.
method Employing Posterior Regularization to impose max-margin constraints on nodes at every level.
result Improves cluster separation in BHMC models, enhancing overall model performance.
We present an approach to model-based hierarchical clustering by formulating an objective function based on a Bayesian analysis. This model organizes the data into a cluster hierarchy while specifying a complex feature-set partitioning that is a key component of our model. Features can have either a unique distribution…
MMGAN stabilizes GANs for multi-modal data clustering.
problem Stability and data distribution loss in GANs for multi-modal data.
method Model latent space as Gaussian mixture model, clusters data manifolds, and trains with clustering network.
result MMGAN outperforms state-of-the-art models in clustering multi-modal data.
The information bottleneck (IB) approach to clustering takes a joint distribution P(X,Y) and maps the data X to cluster labels T which retain maximal information about Y (Tishby et al., 1999). This objective results in an algorithm that clusters data points based upon the similarity of their condit…
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning. Many real life data sets contain a small amount of labelled data points, that are typically disregarded when training generative models. We propose the Cluster-aware Generative Mod…
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.
Model selection in clustering requires (i) to specify a suitable clustering principle and (ii) to control the model order complexity by choosing an appropriate number of clusters depending on the noise level in the data. We advocate an information theoretic perspective where the uncertainty in the measurements quantize…
Generative model for hypergraph clustering improves detection of higher-order structure.
problem Detecting clusters in complex relational systems modeled as hypergraphs.
method Poisson degree-corrected hypergraph stochastic blockmodel (DCHSBM) and Louvain-type algorithms.
result AON hypergraph Louvain algorithm efficiently detects higher-order structure in large hypergraphs.
iCVI-ARTMAP accelerates clustering with adaptive resonance theory and validity indices.
problem Improving clustering efficiency and accuracy using adaptive resonance theory.
method Integrates adaptive resonance theory (ARTMAP) with incremental cluster validity indices (iCVIs) for clustering.
result Significantly reduces clustering time and outperforms other methods on synthetic and real-world data.
We develop a model to cluster time-series data with interval censoring, improving disease phenotyping.
problem Noise and interval censoring hinder clustering in disease phenotyping.
method Deep generative, continuous-time model that clusters time-series data while correcting for censorship.
result Our model corrects for interval censoring and recovers known clinical subtypes.
Algorithm identifies intended fairness constraints from expert demonstrations for fair clustering.
problem Fair clustering challenges due to incomplete fairness constraints.
method Algorithm identifies fairness metric from expert demonstrations and generates clusters.
result Algorithm identifies and generates fair clusters from limited expert demonstrations.
In this paper we formulate in general terms an approach to prove strong consistency of the Empirical Risk Minimisation inductive principle applied to the prototype or distance based clustering. This approach was motivated by the Divisive Information-Theoretic Feature Clustering model in probabilistic space with Kullbac…
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, Dirichlet process mixture models, and Pitman--Yor process mixture models make this assumption, as do all other infinitely exchangeable cluste…
Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clu…
The study compares clustering risk in Hidden Markov and i.i.d. models, showing the Bayes classifier is nearly optimal.
problem Comparing clustering risk in Hidden Markov and i.i.d. models.
method Analysis of Bayes risk, theoretical bounds, and simulations.
result The Bayes classifier is nearly optimal for clustering in both Hidden Markov and i.i.d. models.
MC-GMENN improves neural networks for clustered data using Monte Carlo methods.
problem Improving neural network performance on clustered data with correlations.
method MC-GMENN employs Monte Carlo methods to train generalized mixed effects neural networks.
result MC-GMENN outperforms existing models in generalization and quantifying inter-cluster variance.
A hybrid method clusters and characterizes cancer data efficiently.
problem Challenges in clustering high-dimensional biomedical data.
method Gaussian mixture with generalized factor analyzers for efficient estimation.
result Our approach outperforms existing methods with faster convergence and higher accuracy.
New spectral clustering method for multi-layer networks improves accuracy.
problem Detecting community structure in multi-layer networks.
method Integrative spectral clustering based on adaptive layer aggregation.
result Our methods minimize mis-clustering error and outperform existing methods.
Randomized spectral co-clustering speeds up large-scale directed networks.
problem Co-clustering directed networks efficiently for large-scale data.
method Randomized spectral co-clustering algorithms using random-projection and random-sampling techniques.
result Theoretical and numerical validation of approximation and misclustering error rates.
EGMM improves clustering by better handling uncertainty with evidential framework.
problem Clustering uncertainty and complexity in data.
method Proposes EGMM, a new model-based clustering algorithm using belief functions and EM algorithm.
result EGMM generates more informative evidential partitions and outperforms other algorithms.
Study provides guarantees for kernel clustering under non-parametric mixtures.
problem Statistical guarantees for kernel-based clustering without strong assumptions.
method Non-parametric mixture models, kernel-based clustering, consistency guarantees.
result Necessary and sufficient separability conditions for consistent clustering recovery.