DAC learns to cluster datasets efficiently using neural networks.
problem Efficient clustering of datasets without prior knowledge.
method Deep amortized clustering (DAC) using neural networks to learn clustering.
result DAC efficiently and accurately clusters new datasets from the same distribution.
Convex clustering can only learn convex clusters, with significant gaps between clusters.
problem Understanding the limitations and capabilities of convex clustering.
method Analyzing convex clustering solutions, proving properties, and characterizing clusters.
result Convex clustering can only learn convex clusters with significant gaps between clusters.
Improves clustering fairness by learning fair clusters adaptively.
problem Fairness in deep clustering, especially for protected status variables.
method Formulates group-level fairness as ILP, integrates into discriminative deep clustering, refines learning algorithm.
result Consistently outperforms fair clustering algorithms on real-world datasets.
ABC learns context-aware representations for clustering.
problem Learning latent representations that adapt to context in machine learning.
method Attention-based neural architecture that learns a similarity kernel.
result Competitive results for clustering Omniglot characters.
DPMM-CFL clusters clients for federated learning without fixed K, improving performance.
problem Improving federated learning performance under non-IID client heterogeneity.
method DPMM-CFL uses a Dirichlet Process Mixture Model to infer both cluster number and client assignments.
result DPMM-CFL optimizes per-cluster federated objectives and jointly infers cluster number and assignments.
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.
Improved algorithm for clustered Federated Learning reduces initialization and hyperparameter requirements.
problem Dichotomy between heterogeneous models and simultaneous training in Federated Learning.
method Proposes a new clustering framework and an improved algorithm ( exttt{SR-FCA}) that removes restrictive assumptions.
result Improves clustering accuracy and removes the need for good initialization and hyperparameters.
RKD improves clustering in semi-supervised learning with limited labels.
problem Improving clustering accuracy in semi-supervised learning with few labeled examples.
method RKD as spectral clustering on a teacher model's graph, with clustering error quantification.
result RKD provably leads to low clustering error in semi-supervised classification problems.
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.
Deep clustering has increasingly been demonstrating superiority over conventional shallow clustering algorithms. Deep clustering algorithms usually combine representation learning with deep neural networks to achieve this performance, typically optimizing a clustering and non-clustering loss. In such cases, an autoenco…
FedMCC learns from distributed data to cluster and extract features.
problem Learning from distributed data and clustering.
method Federated Momentum Contrastive Clustering (FedMCC) framework.
result FedMCC outperforms existing methods in linear evaluation and semi-supervised learning.
Transform learning improves K-means clustering for document analysis.
problem Improving K-means clustering for document analysis.
method Embedding K-means clustering loss into transform learning framework and solving jointly using ADMM.
result Improves over state-of-the-art in document clustering.
Paper tackles Byzantine attacks in Federated Learning by clustering and robustifying.
problem Adversarial attacks from Byzantine machines in Federated Learning.
method Iterative Federated Clustering Algorithm (IFCA) with trimmed mean and median aggregation.
result Improved convergence rate for strongly convex loss functions in Byzantine-Robust IFCA.
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.
Proposes a hierarchical clustering method for positive and negative dissimilarities.
problem Clustering dissimilarities, especially positive and negative.
method Hierarchical correlation clustering followed by tree preserving embedding.
result Performance on various datasets.
Transforms data into separable subspaces for clustering.
problem Data is not always separable into subspaces.
method Embeds subspace clustering techniques into transform learning.
result Improves upon state-of-the-art clustering techniques.
AutoEmbedder clusters unlabeled data using semi-supervised DNN embedding.
problem Clustering unlabeled data efficiently and effectively.
method Semi-supervised DNN embedding system using Siamese network architecture.
result AutoEmbedder outperforms existing DNN-based semi-supervised methods.
Improved clustering accuracy with disentangled latent code representation.
problem Improving k-Means clustering performance.
method Optimizing the entanglement of autoencoder latent code representation using soft nearest neighbor loss with annealing temperature.
result 96.2% test clustering accuracy on MNIST, 85.6% on Fashion-MNIST, and 79.2% on EMNIST Balanced datasets.
New approach learns image transformations directly for clustering.
problem Learning better deep representations for image clustering.
method Directly learns transformations and clusters in image space without abstract features.
result Jointly learns prototypes and transformations using deep learning modules.
Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential way, where a deep representation is learned using a deep autoencoder before obtaining clusters with k-means, or a simultaneous way, where dee…
Proposes FMC for fair clustering with independent parameters.
problem Finding clusters with balanced sensitive attribute proportions.
method Model-based clustering using finite mixture model with mini-batch learning.
result FMC scales up easily and can handle non-metric data.
We study in this paper the problem of jointly clustering and learning representations. As several previous studies have shown, learning representations that are both faithful to the data to be clustered and adapted to the clustering algorithm can lead to better clustering performance, all the more so that the two tasks…
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
problem L2SC tackles the challenge of incorporating new spectral clustering tasks without relearning all previous tasks.
method L2SC uses an orthogonal basis library and feature embedding library to selectively transfer knowledge from previously learned tasks to new tasks.
result L2SC outperforms state-of-the-art spectral clustering algorithms on real-world benchmark datasets.
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.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
problem Efficiently clustering data with limited pairwise similarity information.
method Developed principled active learning approach using information-theoretic acquisition functions.
result Significantly outperforms existing baselines in clustering accuracy and query efficiency.
The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which often involves cohesive clustering with a focus on instance relations.…
New algorithm clusters hyperspectral images at multiple scales.
problem Clustering hyperspectral images at various scales.
method M-SRDL algorithm using spectral-spatial diffusion distances.
result More accurate clustering labels achieved with spatial regularization.
Proposes Contrastive Clustering for improved clustering performance.
problem Improving clustering performance on various datasets.
method Instance- and cluster-level contrastive learning through data augmentations and feature space projections.
result Contrastive Clustering achieves significant improvements over 17 competitive methods.
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.
An adaptive clustering algorithm learns from evolving data without manual tuning.
problem Clustering in dynamic data environments where distributions change over time.
method ART-based topological clustering with self-adjusting vigilance parameter.
result The algorithm outperforms state-of-the-art methods in clustering performance and continual learning.
The clustering methods have recently absorbed even-increasing attention in learning and vision. Deep clustering combines embedding and clustering together to obtain optimal embedding subspace for clustering, which can be more effective compared with conventional clustering methods. In this paper, we propose a joint lea…
A new algorithm for efficient clustered federated learning.
problem Federated learning with clustered users having different learning tasks.
method Iterative Federated Clustering Algorithm (IFCA) that alternates cluster estimation and model parameter optimization.
result IFCA converges with good initialization and guarantees optimal statistical error rate.
We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs. We propose Non-linear Attributed Graph Clustering by Symmetric Non-negati…
The paper explores fair clustering, a niche area in machine learning.
problem Fairness in clustering remains underexplored despite its importance.
method Assesses existing work and proposes new directions for fair clustering.
result Widening normative principles and knowledge of downstream processes can enhance fair clustering research.
A new deep clustering model learns both clustering and embedding simultaneously.
problem Optimizing autoencoders for clustering and embedding separately.
method Integrating clustering module into a deep autoencoder.
result Joint learning of clustering and embedding improves performance.
Elastic co-clustering improves clustering of single-cell genomic data.
problem Improving clustering performance of single-cell genomic datasets.
method Elastic coupled co-clustering in an unsupervised transfer learning framework.
result Our algorithm significantly improves clustering performance over traditional methods.
DPSOM combines self-organizing maps with deep learning for better data clustering.
problem Improving clustering performance in complex data.
method Integrates self-organizing maps with probabilistic clustering using a VAE.
result DPSOM outperforms current deep clustering methods in various applications.
Meta-learning neural networks for better clustering representations.
problem Improving clustering performance with appropriate representations.
method Meta-learning method that trains neural networks for representations using VB inference with an infinite Gaussian mixture model.
result The method achieves higher clustering performance than existing methods.
A novel method for clustering multi-view data using dual representations.
problem Clustering multi-view data with consistent and unique information.
method One-step multi-view clustering method exploiting dual representations.
result The proposed method improves clustering performance on benchmark datasets.
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.
Proposes a new method for clustering tasks in multi-task learning.
problem Improving performance of multiple related prediction tasks.
method Semisoft task clustering approach with a three-step algorithm.
result Validation of the proposed approach on synthetic and real-world datasets.
The paper extends fairness to hierarchical clustering, finding efficient algorithms with minimal loss.
problem Ensuring fairness in hierarchical clustering where data is recursively partitioned.
method Extending fairness to hierarchical clustering, developing simple, efficient algorithms for various objectives.
result Simple, efficient algorithms for fair hierarchical clustering with only a negligible loss in objective.
Graph based clustering is one of the major clustering methods. Most of it work in three separate steps: similarity graph construction, clustering label relaxing and label discretization with k-means. Such common practice has three disadvantages: 1) the predefined similarity graph is often fixed and may not be optimal f…
Tree Index evaluates cluster quality by creating decision trees from data.
problem Evaluating the quality of cluster results from various techniques.
method Tree Index creates a decision tree from clustered data, combining entropy and depth of leaves.
result Tree Index discriminates between sensible and non-sensible clusters on brain dataset.
A new framework learns clustering and dimensionality reduction together.
problem Challenges in clustering high-dimensional data.
method Gradient-based manifold optimization for joint learning.
result Better performance compared to existing clustering algorithms.
Determining the quality of the results obtained by clustering techniques is a key issue in unsupervised machine learning. Many authors have discussed the desirable features of good clustering algorithms. However, Jon Kleinberg established an impossibility theorem for clustering. As a consequence, a wealth of studies ha…
Unified Bayesian framework for clustered federated learning improves model performance.
problem Handling non-IID client data in federated learning.
method A unified Bayesian framework for clustered federated learning that associates clients to clusters and proposes practical algorithms for data associations.
result The proposed framework increases model performance by circumventing the need for unique client-cluster associations.
Proposes RNSE for clustering with adaptive similarity matrix learning.
problem Sub-optimal results due to mismatch between stages in Spectral Clustering.
method End-to-end single-stage learning with adaptive similarity matrix and non-negative constraints.
result Superior clustering performance on synthetic and real-world datasets.