Unsupervised Clustering

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11 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 85

Oct 7, 2026cs.LG

Leaner Transformers Can Easily Learn to Cluster

Transformers have in-context learning capabilities, where some known learning algorithms can be executed in the forward pass through the model. Recent work shows that transformers can exactly perform Lloyd's algorithm for

k

-means clustering with

n

points in

d

dimensions with an embedding size

d_{\textsf{emb}} = d+k

(thus, requiring attention projection matrices of size

(d+k)^2

). In this work, we build upon this result in the following ways: First, we present an equally expressive but smaller transformer that executes Lloyd's algorithm with embedding size

d_{\textsf{emb}} = (d + \lceil \log_2 k \rceil)

. Next, we train these transformers to learn the clustering algorithms given a distribution of clustering tasks, and theoretically characterize and empirically validate the factors affecting the convergence and in-distribution generalization of learning algorithms based on stochastic gradients. Finally, we probe the general clustering abilities of these learned algorithms (in the form of transformers), and try to understand situations where they succeed and fail.

Charlotte Park, Kenneth L. Clarkson, Lior Horesh +2

MIT · IBM Research

Transformer Clustering In-Context Learning Unsupervised Clustering

Sep 29, 2026cs.NE

Simulating Synchrony Loop Networks in the Open Source RISP Neuroprocessor

Neuromorphic spiking neural networks (SNNs) offer a promising alternative to conventional deep neural networks for tasks with computational resource or data constraints. However, their practical applications have been limited by comparatively weak performance on complex learning tasks. Experimental approaches such as Synchrony Loop Propagation (SLP) increasingly seek to address this problem through more sophisticated and heterogeneous neuron models, and have achieved encouraging initial results. However, these neuron models do not readily translate to standard neuromorphic systems designed to support simple leaky integrate-and-fire neurons. We present an implementation of an SLP network on the RISP neuroprocessor, an event-driven neuromorphic simulation platform, and evaluate its performance on an unsupervised musical instrument clustering task. The network achieves clustering accuracy comparable to the DBSCAN algorithm, while providing over an order of magnitude improvement in runtime speed and computational efficiency relative to a prior non-neuromorphic SLP implementation. These results demonstrate that SLP's core mechanisms can be effectively translated into a neuromorphic architecture to support complex unsupervised learning. More broadly, this work highlights the potential of heterogeneous and extensible neuron models to expand the design space of neuromorphic systems to more complex learning tasks.

Jackson Mowry, Patrick Abbs

EECS Department University of Tennessee Knoxville, TN USA · Cambrya, Inc. Austin, TX USA

Unsupervised Clustering Spiking Neural Networks Neuromorphic Computing

Sep 23, 2026stat.ML

Selective Inference for Deep Clustering in Latent Spaces

Deep clustering is a powerful approach for discovering meaningful structures in high-dimensional data by learning a low-dimensional latent representation prior to clustering. Despite its empirical success, assessing the statistical reliability of the resulting clusters remains challenging. Testing discovered clusters on the same data induces selection bias and invalidates classical

p

-values. Selective inference (SI) provides a principled framework for correcting this bias, but existing methods focus on clustering performed directly on the observed features. In this work, we develop an SI framework for deep clustering with a fixed pretrained encoder. The key challenge is that cluster assignments are determined through a nonlinear transformation from the original data space to the latent space, resulting in a substantially more complex selection process than in conventional clustering. Our method provides a computationally tractable way to account for this process and enables valid statistical testing of differences between clusters identified in the latent space. Synthetic experiments demonstrate that the proposed method controls the Type I error rate while achieving higher power than valid but conservative baselines, and genomic applications show that it can identify significant cluster differences while appropriately accounting for selection bias. Our framework provides a principled approach to quantifying the statistical reliability of structures discovered by deep clustering.

Eina Mizui, Tomohiro Shiraishi, Shunichi Nishino +1

Nagoya University · RIKEN

Selective Inference Clustering Unsupervised Clustering Deep Clustering Statistical Hypothesis Testing

Sep 22, 2026stat.ME

Automatic depth-based local center clustering via $β$ -integrated local depth and adaptive grouping

Clustering is an unsupervised learning technique that partitions unlabeled data into groups. Most existing methods require user-specified parameters, such as the number of clusters or neighborhood size. Conversely, we propose automatic depth-based local center clustering (A-DLCC), a fully data-driven method that eliminates numerical parameter tuning. A-DLCC uses the

β

-integrated local depth to identify stable exemplars, points consistently central across multiple locality levels, termed local centers, which are ranked by their representativeness. Each local center induces a group of similar points, with group-level similarity measured by a proposed nonparametric metric called group-level local similarity. To guide merging, we incorporate the bottleneck path idea from graph theory, which forms the basis of our adaptive merging criterion. Based on this criterion, we design a single agglomeration rule in which a group is either absorbed by a neighbor it reaches better than itself or bonded to a neighbor that both sides find more reachable than their own background, every merge being additionally required to be carried by a contact stronger than a configuration-model null expects. The rule automatically estimates the number of clusters and decides when to stop merging. Experiments on synthetic and real data show that A-DLCC produces interpretable clustering results without parameter tuning.

Siyi Wang, Alexandre Leblanc, Paul D. McNicholas

Clustering Hierarchical Clustering Unsupervised Clustering

Sep 22, 2026cs.LG

Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy

Multi-view clustering (MVC) aims to uncover latent cluster structures by exploiting complementary information from multiple views. Despite substantial progress in clustering performance, fairness remains an important concern when MVC is applied to socially sensitive scenarios. Recent fair multi-view clustering methods have introduced fairness constraints into representation learning or clustering assignments. However, these methods generally treat different views under a largely uniform fairness mechanism, without explicitly distinguishing their varying levels of sensitive dependence during cross-view learning. In practice, different views may encode substantially different levels of sensitive information. Ignoring such cross-view discrepancy can allow highly sensitive-dependent views to influence less sensitive-dependent ones during cross-view learning, potentially degrading both clustering performance and fairness. To address this issue, we propose a novel multi-view fair clustering framework guided by cross-view sensitive information discrepancy. Specifically, we estimate the sensitive dependence of each view and develop a bias-ranked asymmetric alignment mechanism that encourages views with higher sensitive dependence to learn from those with lower sensitive dependence, while cross-view discrepancies are further exploited to adaptively regulate the alignment process. Moreover, fairness regularization is imposed on the consensus soft assignments to further promote group fairness. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves a favorable balance between clustering quality and group fairness.

Mudi Jiang, Jiahui Zhou, Xinying Liu +2

School of Software, Dalian University of Technology, Dalian, Liaoning, China

Multi-View Learning Unsupervised Clustering Multi-View Clustering Group Fairness

Sep 21, 2026cs.LG

G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation

We introduce Graph Neural Automata Clustering (G-NAC), an unsupervised clustering method in which observations interact as cells on a fixed neighborhood graph. A shared recurrent graph-neural cellular rule evolves latent domain states through local interactions, which are converted into a rank-based spectral affinity for partitioning. Across 73 clustering tasks from 57 benchmark datasets, G-NAC achieved a mean adjusted Rand index (ARI) of 0.7951, comparable to Genie at 0.7941 and higher than the other evaluated baselines. Empirical training time and GPU memory scaled approximately linearly from 5,000 to 100,000 nodes. Learned transition rules also transferred from smaller source graphs to independent 100,000-node samples generated under matched conditions. These results demonstrate a recurrent graph-clustering formulation while identifying dependencies on graph quality, readout design, and source-target similarity.

Keith Miller, Tristan Crawford

Graph Neural Networks Unsupervised Clustering Graph Clustering

Sep 21, 2026cs.LG

RAILS: Retrieval-Augmented Incremental LLM Clustering at Scale

Using a Large Language Model (LLM) as the clusterer at production scale is hard: prompts cannot hold the entire label space, and per-document serial processing does not deliver the throughput real workloads require. We present RAILS, a retrieval-augmented incremental LLM clusterer that turns clustering into a simple loop over a growing label pool and scales through document batching with bounded concurrency. On six public benchmarks RAILS exceeds the strongest prior LLM-clustering method on average, lifting accuracy from 51.2% to 59.3%, NMI from 67.2% to 74.8%, and ARI from 45.4% to 54.7%. We further report production-deployment evidence from a SaaS ticket-topic-discovery pipeline, where RAILS has replaced a traditional HDBSCAN stage with higher clustering quality, transparent prompt-driven control, and stateful incremental operation.

Armin Oliya, Aleksandra Sawczuk, Radosław Białobrzeski

Zendesk

Clustering Unsupervised Clustering

Sep 15, 2026stat.ML

Bracketing Uncertainty in Clustering Under the Manifold Hypothesis

The manifold hypothesis suggests a natural criterion for clustering: partition data according to the manifold component from which each point is drawn. Whether two components are separable depends on a geometric tradeoff: the ambient separation between components versus the largest gap in sampling. In practice, this tradeoff is rarely assessed explicitly, leading standard methods to over-commit to a single clustering assignment even when the data do not support a unique answer. We formalize this tradeoff by combining intrinsic manifold geometry (volume growth and reach) with sample-level quantities (fill distance and density), yielding a threshold phenomenon for mutual-

k

-nearest-neighbor graphs: when the offset-to-fill ratio exceeds a conservative upper threshold, component separation is preserved; below a lower threshold, components fuse. The gap between these thresholds defines a geometric uncertainty zone in which the number of clusters is not identifiable from the data. Nevertheless, conventional approaches still seek one: sweeping parameters (an engineering approach) or fitting a generative mixture model (a model-based approach). Rather than forcing a single estimate of the number of clusters, we propose Manifold-Based Clustering (MBC), which returns an explicit bracket interval to quantify the underlying data uncertainty. This bracket acts as an empirically calibrated diagnostic: it narrows when a single resolution is supported, widens when multiple resolutions coexist, and collapses to one when no separated structure is detectable. Empirically, we find that many real datasets lie within the uncertainty zone rather than admitting one clear answer. Our results suggest that ambiguity in cluster number is often intrinsic, and should be quantified rather than resolved.

Savik Kinger, Luciano Dyballa, Steven W. Zucker

Department of Computer Science Yale University · School of Science & Technology IE University · Depts. of Computer Science and Biomedical Engineering Wu Tsai Institute Yale University

Clustering Unsupervised Clustering Manifold Learning

Sep 15, 2026cs.CV

InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy

Label-free clustering of frozen pretrained visual embeddings offers a scalable route to biodiversity monitoring, but image-only fine-grained taxonomy exhibits a consistent coarse-to-fine failure mode: clusters recover broad taxonomic structure yet plateau at species level. We study this behaviour on BIOSCAN-5M through an information-calibrated clustering analysis. BioCLIP~2 features with UMAP and HDBSCAN reach

0.79

AMI at family and

0.67

at genus, substantially improving over the prior image baseline and remaining competitive with oracle-

K

, graph-based, and learned clustering heads on the same frozen features. To diagnose whether the remaining plateau is method-limited or information-limited, we introduce InfoTaxa, which combines clustering efficiency---the fraction of probe-estimated image information recovered by an unsupervised partition---with paired DNA as an audit signal only, not an inference input. The density pipeline recovers approximately

0.90

and

0.81

of the image-available information at order and family, respectively. Held-out late-fusion probes show that adding DNA to the image embedding reduces species-level prediction error by approximately two bits. Robustness analyses cover multiple image encoders, described-species and rare-class subsets, probe diagnostics, and held-out-species coarse-rank generalisation and same-species retrieval. Thus, in the tested setting, species-level label-free clustering is both clustering-limited and representation-limited: improved clustering may recover additional image-exposed structure, but cannot close the DNA-audited information gap alone.

David Ahmedt-Aristizabal, Mohammad Ali Armin, Lars Petersson

Imaging and Computer Vision Group, CSIRO, Australia

Visual Representation Learning Clustering Unsupervised Clustering Image Clustering

Sep 14, 2026stat.ML

Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data

Diagnostic errors and mislabeling are common in biomedicine, which compromise the reliability of predictive models and data-driven outcomes. Stratifying unlabeled biomedical data based on complex relationships between features eliminates the need for data labels and overcomes the limitations of supervised learning. Traditional clustering methods assume restrictive data distributions, making them suboptimal for capturing complex dependencies in high-dimensional biomedical data. This paper introduces a novel cluster-friendly data presentation framework that integrates the non-Gaussian and non-linear feature dependence of copula models with an ensemble of causal structure discovery (CSD) methods based on Directed Acyclic Graphs (DAGs). While copulas model flexible multivariate distributions by relaxing assumptions related to multivariate normality, linear dependence, and symmetric relationships, an ensemble of DAG-based CSD methods identifies stable causal relationships between features. When clustered using K-means, the new data representation obtained by the proposed copula-adapted DAG (CopDAG) ranks first among the 12 methods in normalized clustering accuracy and adjusted Rand index across 16 biomedical datasets. Our CopDAG method predicts ground-truth class labels directly from feature relationships without data annotations and supervised learning, while also providing cluster visualizations and explainable causal structures of the biomedical data features.

Heranga K. Rathnasekara, Norou Diawara, Manar D. Samad

Department of Mathematics and Statistics Old Dominion University, Norfolk, VA 23529, USA · Department of Computer Science North Carolina Agricultural and Technical State University Greensboro, NC 27411, USA

Copula Models Unsupervised Clustering

Sep 11, 2026cs.CL

From Repetition to Recognition: Inductive Discovery of Disinformation Narratives

In disinformation datasets, narratives are often understood as recurring interpretive patterns that group texts under narrative labels. Recent work formalized narrative mining as inductively inferring narrative labels from corpora, but its evaluation stays tied to predefined taxonomies, a closed-world setting that cannot capture narratives absent from the reference labels. We introduce a three-tier evaluation framework for unsupervised narrative label generation: recovery (against a corpus's own taxonomy), mining (against external label sets), and discovery (without predefined labels). Applying it, we compare clustering-based and graph-community-based pipelines across seven disinformation datasets, with human validation of discovery on two. The two families are complementary under automated metrics, but in a corpus with two prominent topics, clustering can reduce one topic to 2% of generated labels while graph-based pipelines stay balanced. Discovery validation also reveals many singletons (narrative labels derived from single claims, 30-62% of graph outputs), which clustering cannot produce. Annotators confirm many as recognizable disinformation narratives, suggesting that in open-world discovery the repetition assumed by narrative mining may be recognized outside the corpus, not within it. We release human-validated narrative candidate labels for the Climate Obstruction and PolyNarrative datasets to support taxonomy development and dataset extension.

Max Upravitelev, Veronika Solopova, Jing Yang +4

Technische Universität Berlin · German Research Center for Artificial Intelligence (DFKI) · BIFOLD – Berlin Institute for the Foundations of Learning and Data +2

Unsupervised Clustering

Sep 7, 2026cs.LG

CLUES-WEASEL: No additional clues required to choose your time series clustering algorithm

Time series data is very common in many real-world applications and in numerous domains, with increasing interest for automated information extraction using machine learning. One of these subfields is time series clustering, which consists in identifying clusters among a set of time series in an unsupervised fashion. Most time series clustering algorithms suffer from the same balancing act: they trade clustering performance for faster runtimes or vice versa. We present a novel time series clustering algorithm that we call CLUES-WEASEL, which stands for CLustering with the UnsupervisEd Second version of Word ExtrAction for time SEries cLassification. CLUES-WEASEL extracts features using the unsupervised version of the transformation step of WEASEL 2.0, which is a time series classification algorithm, then reduces these features using principal component analysis, and finally performs clustering with the

k

-means algorithm using these reduced extracted features. Through extensive experiments, we prove that CLUES-WEASEL is significantly better than any other existing time series clustering algorithm while being (much) faster than any state-of-the-art one. We also show that the architecture of CLUES-WEASEL can work well with other time series feature extraction algorithms. Our findings highlight the relevance of CLUES-WEASEL for time series clustering.

Johann Faouzi

Univ Rennes, Ensai, CNRS, CREST - UMR 9194, F-35000 Rennes, France.

Clustering Unsupervised Clustering Time Series Clustering

Aug 19, 2026cs.LG

Cluster Assignments in Soft Targets Shape Speech Representations: Evidence from S-JEPA

Cluster-based prediction is widely used in self-supervised speech learning. A soft target preserves a distribution over clusters rather than a single label. This distribution specifies both the probability values and which clusters receive them. Comparisons between soft targets and hard labels do not separate the contributions of these two aspects to the learned representation. We study this in S-JEPA, a recent high-performing self-supervised speech model trained with soft Gaussian mixture model (GMM) targets. We compare its original targets with counterfactual targets that preserve the most likely cluster and all probability values but change which remaining clusters receive the other probabilities. Across three training seeds, the original soft distribution is recovered more accurately from Encoders trained with the original than counterfactual targets. Because this could reflect target matching alone, we also test low-level acoustic and phonetic information. Both are more accessible from Encoders trained with the original targets. This suggests that cluster assignments affect acoustic and phonetic properties of the learned representation, not just recovery of the training target.

Wenxuan He, Yunpeng Li, Zewei Li +4

Department of Intelligent Science, School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China

Soft-Label Learning Unsupervised Clustering Predictive Representation Learning Self-Supervised Speech Representation Learning Discrete Speech Representation Learning

Aug 12, 2026cs.LG

Uncertainty-Aware Probabilistic Constrained Clustering from Entangled Pairwise Supervision

Pairwise constrained clustering typically relies on hard must-link/cannot-link labels, whereas realistic pairwise supervision may be real-valued and entangle intrinsic ambiguity, expert judgment, and stochastic corruption. Existing deep constrained clustering (DCC) methods mainly target hard, expert-agnostic constraints, treating soft labels mostly numerically rather than semantically. We formalize this setting as uncertainty-aware probabilistic constrained clustering (UPCC), defining a canonical aleatoric target through a heterogeneous observation process and analyzing its conditional identifiability. We introduce ProbPair, an angular pairwise objective for probabilistic relations, and build ECI-PP, an estimator--corrector--integrator framework that refines imperfect supervision via belief estimation, correction, and reliability-aware integration. Across challenging probabilistic supervision settings, experiments on diverse benchmarks show that ECI-PP outperforms state-of-the-art DCC methods and remains robust with a shared default configuration.

Shaojie Zhang, Ke Chen

Department of Computer Science, The University of Manchester, Manchester M13 9PL, U.K.

Clustering Unsupervised Clustering Learning with Noisy Labels

Aug 9, 2026stat.ML

Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery

Kernel spectral clustering with a single bandwidth can be inadequate for data exhibiting multiple characteristic pairwise-distance scales, a problem particularly prevalent in the high-dimensional regime. We address this issue through a multi-kernel formulation that aggregates kernels with different bandwidths. The bandwidths are selected as prescribed empirical quantiles of the pairwise squared distances, thereby capturing the relevant distance scales without requiring prior population-scale information. We develop a rigorous theoretical analysis of the resulting method under a general high-dimensional, multi-scale mixture model with heterogeneous cluster centers and covariance geometries. We construct a blockwise constant, low-rank informative approximation to the empirical multi-kernel matrix and establish row-wise

\ell_{2,\infty}

perturbation bounds for its leading spectral components, as well as for the associated normalized Laplacian matrix. These bounds yield observation-level control of the spectral embedding, which is more informative than conventional global eigenspace perturbation estimates. Under suitable eigen-gap and cluster-separation conditions, we show that approximate

K

-means applied to the multi-kernel spectral embedding achieves exact recovery with high probability.

Zeqin Lin, Guangming Pan, Zhixiang Zhang +1

1Nanyang Technological University · University of Macau · 3Dalian University of Technology

Clustering Unsupervised Clustering Spectral Clustering Kernel Methods Spectral Methods

Aug 8, 2026cs.AI

GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols. Traditionally, algorithms rely entirely on dataset-internal statistics to estimate categorical relationships, which confines the learned metric to empirical co-occurrences and ignores conceptually obvious yet statistically unobserved affinities. Although LLMs offer external world knowledge, applying their text-centric reasoning to highly abstract tabular concepts presents significant challenges. Bridging this modality gap to construct a semantically complete metric typically requires embedding LLMs into iterative metric learning loops to dynamically optimize cross-modality representations. This incurs intractable computational overhead, forcing a compromise between semantic enrichment and scalability. Therefore, we propose GRACE, an LLM-grounded framework for scalable mixed-data clustering. GRACE shifts semantic acquisition to the attribute-value level via a multi-perspective LLM querying strategy, mapping heterogeneous values into knowledge-informed descriptions. Crucially, this one-shot grounding extracts general-purpose semantic representations that embed heterogeneous attributes into a unified space, decoupling expensive LLM invocation from iterative optimization. Furthermore, GRACE cross-validates these external semantics against dataset-internal statistical evidence to ensure alignment with the dataset-specific cluster structure. Ultimately, GRACE matches the scalability of conventional statistics-driven baselines while achieving superior clustering accuracy and conceptual interpretability over 11 competing methods. The source code is available at https://github.com/develop-yang/GRACE-GRACE-A

Zihua Yang, Zhencheng Xie, Junyang Chen +4

Guangdong University of Technology · Tsinghua University · Peking University +2

Clustering Unsupervised Clustering Metric Learning Tabular ML

Aug 6, 2026q-bio.NC

Convergent Evolution in Neural Representation Space: Emergent Order in Deep Belief Networks

Deep Belief Networks (DBNs) learn hierarchical generative models without class supervision. Here, we ask whether this purely unsupervised process nevertheless organizes internal representations according to the unknown data classes. We analyze successive layers of DBNs trained on MNIST, Fashion-MNIST, and KMNIST using the Generalized Discrimination Value (GDV), supervised probes applied only after training, a reconstruction-based measure of abstraction distance, effective dimensionality, and free sample generation. Remarkably, class-specific clustering generally increases with depth across datasets and network widths, although no label information is available during DBN training. Control experiments show that this effect depends on the learned feature structure and cannot be explained by random transformations, weight marginals, dimensionality reduction, or sigmoid saturation. The first hidden layers also frequently make class identity more accessible to linear and nonlinear probes. With greater depth, representations become increasingly compact and prototype-like as neurons acquire correlated feature directions. At the same time, GDV and probe accuracy reveal complementary aspects of class structure: improved average clustering can coexist with reduced accessibility for a few difficult class pairs. These findings demonstrate that layer-wise generative learning can spontaneously uncover and progressively amplify class-related structure in unlabeled data.

Patrick Krauss, Achim Schilling, Andreas Maier +2

Cognitive Computational Neuroscience Group, Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany · Neuromodulation and Neuroprosthetics, University Hospital Mannheim, University Heidelberg, Germany · Neuroscience Lab, University Hospital Erlangen, Germany +1

Representation Geometry Hierarchical Representation Learning Unsupervised Clustering Unsupervised Representation Learning

Aug 6, 2026cs.LG

Beyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation

Interpreting clustering outcomes remains a fundamental challenge in data analysis, particularly in domains such as healthcare where meaningful patterns must be extracted from high-dimensional data. While numerous explainability techniques exist, they are primarily designed to assess feature importance or provide local instance-level explanations rather than to identify structured patterns present within clusters. This work presents a comparative evaluation of commonly used post-hoc analysis methods for pattern detection in clustering results. To enable controlled evaluation, we introduce a suite of synthetic datasets in which predefined patterns are systematically injected. Three widely used techniques are evaluated: a Random Forest surrogate model with permutation feature importance, LIME (Local Interpretable Model-agnostic Explanations), and principal component analysis. Results demonstrate that although each method can successfully recover relevant features, none consistently detects all injected pattern types. These findings high- light a critical gap between existing explainability tools and the requirements of pattern-level cluster interpretation, motivating the development of dedicated pattern detection methodologies.

Benjamin Connor, Anna Jurek-Loughrey, Lu Bai +1

School of EEECS Queen’s University Belfast 18 Malone Road, Belfast, BT9 5AF

Clustering Unsupervised Clustering Explainability Evaluation

Jul 31, 2026cs.LG

Ensemble of Unsupervised Deep Learning for Clustering Imbalanced Tabular Data

Data imbalance poses a major challenge in supervised classification, where the majority-class bias contributes to false negatives and overestimates classification accuracy. Unsupervised deep clustering can be immune to class imbalance because representation learning for clustering is performed without class labels. Deep clustering has been proposed for images, languages, and graphs, while its application to tabular data has only emerged recently. This paper is among the first to examine the performance of state-of-the-art deep clustering methods under varying levels of data imbalance. We introduce two novel cluster ensemble approaches: one aggregates deep clustering assignments across different embedding dimensions, and the other applies majority voting to the best-performing clustering algorithms. Experiments on 16 binary tabular datasets with varying and artificially induced levels of imbalance reveal distinct strengths of different deep clustering methods. On average, our ensemble methods outperform individual clustering methods in ACC, NMI, and ARI scores, offering greater resilience to data imbalance when identifying ground-truth classes without supervision. Therefore, in an imbalanced data scenario, deep clustering can serve as a strong alternative to supervised classification.

Pulock Das, Yina Hou, Md. Kamrozzaman Bhuiyan +1

Department of Computer Science Tennessee State University Nashville, TN, USA · Enosis Solutions Dhaka, Bangladesh · Department of Computer Science North Carolina A&T State University Greensboro, NC, USA

Class-Imbalanced Learning Clustering Unsupervised Clustering Deep Clustering Tabular ML

Jul 28, 2026cs.LG

When Does Deep Representation Learning Help Single-Cell Clustering? A Sensitivity-Aware Diagnostic Benchmark for Biomedical AI Pipelines

Single-cell ribonucleic acid sequencing (scRNA-seq) is a foundational technology for precision-medicine workflows that contribute to United Nations Sustainable Development Goal 3 on Good Health and Well-being, and unsupervised clustering is the analytical step that turns raw expression matrices into interpretable cell populations. Practitioners therefore face a recurring engineering decision: is an additional deep representation stage worth its compute and tuning cost, or do classical principal component analysis (PCA) pipelines already suffice? We address this question with a diagnostic benchmark of nine clustering pipelines on ten real datasets (90-5,685 cells, 19,046-41,480 genes, 4-11 cell types), augmented by a partial scVI V2 specialized comparison on seven datasets. The protocol integrates Optuna hyperparameter search, repeated-run robustness, Friedman/Wilcoxon-Holm/TOST testing, and Sobol total-order sensitivity analysis. The contrastive autoencoder achieved the highest mean Adjusted Rand Index (0.7872), but Holm-corrected tests did not establish dominance over the strongest baselines. Per-dataset analysis reveals three reproducible regimes: probabilistic variational autoencoder (VAE) variants help on the smallest datasets, deep autoencoders win on mid-scale data with multi-batch or many-type structure, and classical PCA pipelines remain competitive when linear projection already captures the dominant variation. Sobol indices identify learning rate (

S_T=0.70

) and latent dimensionality (

S_T=0.56

) as the dominant variance contributors, indicating where limited tuning budgets should be allocated. The contribution is therefore a dataset-aware and compute-conscious decision framework for biomedical AI pipelines supporting sustainable healthcare analytics, rather than a universal superiority claim.

Nguyen Thanh Phong, Truong Viet Vu, Nguyen Ha Thu +4

Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam · Faculty of Law, Van Lang University, Ho Chi Minh City, Vietnam

Representation Learning Clustering Unsupervised Clustering Single-Cell RNA Sequencing

Jul 28, 2026stat.ML

Lloyd's $K$ -Means Clustering Algorithm Is Frank-Wolfe in Disguise

Lloyd's

K

-means algorithm, also known as naïve

K

-means, is a widely used ad hoc optimization heuristic, designed to minimize the sum of squared errors (SSE) across all

K

-partitions of a dataset via iterative cluster refinement. In this work, we establish a novel connection between Lloyd's algorithm and the Frank-Wolfe (FW) algorithm, a prominent first-order method for projection-free optimization. We demonstrate that Lloyd's algorithm is a special case of FW. Leveraging recent advances in FW methods for concave objectives, we derive a non-asymptotic

\mathcal{O}(1/t)

convergence rate to a local minimum of the SSE objective. To account for empty clusters, an outcome possible under Lloyd's greedy assignment, we develop an FW variant for semismooth objectives while retaining the same convergence rate that is solely controlled by the initial SSE value. We illustrate our findings with a simulation study for spherical Gaussian mixtures and a real-world image segmentation dataset.

Michael Pokojovy, J. Marcus Jobe, Simon Lacoste-Julien

Department of Mathematics & Statistics and School of Data Science Old Dominion University Norfolk, VA 23529, USA · Information Systems & Analytics Department Miami University Oxford, OH 45056, USA · Canada CIFAR AI Chair Department of Computer Science and Operations Research & Mila Université de Montréal Montréal (QC) Canada

Clustering Unsupervised Clustering Nonconvex Optimization

Jul 27, 2026cs.LG

The K-SCAN Clustering Algorithm

In the Big Data era, the scalability of clustering algorithms constitutes a key challenge. Traditional density-based methods (e.g., DBSCAN) offer robustness to noise and the ability to detect non-linear clusters, yet their quadratic time complexity

O(N^2)

drastically limits their applicability. Conversely, partitional algorithms (e.g., K-Means), with their linear complexity

O(N)

, impose sphericity on the resulting groups and fail in the presence of outliers. This paper presents K-SCAN -- a novel hybrid algorithm that optimizes this trade-off. The method integrates preliminary vector quantization (stochastic Mini-Batch K-Means) to extract a reduced set of weighted micro-clusters, followed by a subsequent density-based structural analysis. Empirical evaluation on datasets of up to

10^6

samples confirms the linear computational complexity of the proposed solution. K-SCAN achieves more than a 3-fold speed-up over the hierarchical BIRCH algorithm, avoiding the costly management of tree-based structures. The method precisely identifies non-linear manifolds while maintaining structural stability (Adjusted Rand Index > 0.99), even with noise levels reaching 55% of the data volume. The main limitation of the proposed algorithm, which could not be fully eliminated in the present study, remains its susceptibility to over-smoothing and its difficulty in separating clusters with highly heterogeneous local density. In complex visual spaces, this can lead to the loss of the finest topological details.

Filip Kosiorowski, Grzegorz Sroka

The Faculty of Mathematics and Applied Physics, Rzeszów University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland · Department of Analysis Nonlinear, Rzeszów University of Technology, Powstańców Warszawy 12, 35-959 Rzeszów, Poland

Clustering Unsupervised Clustering

Jul 27, 2026cs.LG

Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory

Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point similarity function to find similar points. This works well for compact clusters, but clustering performance can deteriorate badly when cluster shapes are irregular, and densities or sizes vary between clusters. Recent `Cluster-as-Distribution' (CaD) clustering has been shown to discover these generic types of clusters in practice by treating each cluster as a set of independent and identically distributed points generated from some unknown distribution via a greedy search, achieving a clustering objective equivalent to that of Spectral Clustering, but with better clustering outcomes without eigen-decomposition. However, a theoretical analysis of this phenomenon is still lacking. Our analyses are from two angles. First, we analyze the approximation error between the true and empirical distribution embeddings. Second, we show that the greedy search employed to achieve the CaD clustering objective can be mapped to a partition matroid---yielding greedy optimality. These yield a near-optimality guarantee for the CaD clustering objective, with regret controlled by the approximation error. This is the first analysis that explains why CaD clustering via greedy search can discover clusters of arbitrary shapes, densities and sizes (where all set-oriented clustering methods have failed to discover) when the estimated cluster embeddings faithfully approximate the underlying cluster distributions.

Kaifeng Zhang, Kai Ming Ting, Sanjay Chawla

School of Artificial Intelligence Nanjing University, China · Qatar Computing Research Institute, HBKU

Clustering Unsupervised Clustering

Jul 21, 2026cs.LG

SCPP: A Unified Python Library for Soft Clustering

In this paper, we present SCPP (Soft Clustering Python Package), an open-source Python framework for soft clustering. SCPP establishes a canonical, scikit-learn-compatible estimator interface that standardizes model training, prediction, membership representation, evaluation, and benchmarking across heterogeneous soft clustering methods, including fuzzy, probabilistic, graph-based, matrix factorization, and deep learning methods. The framework currently integrates 40 representative algorithms together with a comprehensive benchmarking comprising datasets, clustering quality metrics, and standardized runtime, memory, and scalability evaluation. SCPP further provides extensive documentation, practical examples, automated testing, and seamless integration with the scientific Python ecosystem, enabling reproducible experimentation and straightforward extension with new algorithms. The source code is publicly available at https://github.com/soft-clustering/soft-clustering.

Kiyan Rezaee, Morteza Ziabakhsh, Artin Bahrampour +6

Clustering Unsupervised Clustering

Jul 21, 2026cs.LG

An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction

Breast cancer is one of the most widespread types of cancer, affecting approximately 8 million women worldwide. Electronic health records of patients diagnosed with this disease can serve as valuable datasets for computational analyses, enabling the discovery of new insights about the pathology. Unsupervised clustering, in particular, can identify groups of patients with medically significant features, revealing data trends that might otherwise go unnoticed by medical doctors. In this study, we first applied the DBSCAN density-based clustering method to three independent datasets derived from electronic medical records of patients with mammary carcinoma. Subsequently, to enhance our results, we preceded the DBSCAN application with a dimensionality reduction phase using UMAP. We evaluated our clustering outcomes using three statistical indices (DBCV, DCSI, and DISCO). Our results confirm the effectiveness of combining UMAP with DBSCAN for clustering data derived from electronic health records, paving the way for the medical interpretation of the patient groups identified by our approach.

Davide Chicco, Nicoletta Benvenuto

Dipartimento di Informatica Sistemistica e Comunicazione, Universit`a di Milano-Bicocca, Milan, Italy. · Institute of Health Policy Management and Evaluation, University of Toronto, Toronto, Ontario, Canada.

Breast Cancer Clustering Unsupervised Clustering

Jul 21, 2026cs.LG

Unsupervised Multi-kernel Learning for Automated Algorithm Selection

Automated algorithm selection in black-box optimization typically relies on supervised models that map landscape features to algorithm performance labels. Such models are costly to train, benchmark-dependent, and often fail to generalize to unseen problem classes. We study an unsupervised alternative: multi-kernel clustering over heterogeneous landscape representations, in which problem instances are grouped without using performance labels in the clustering stage, and the resulting clusters are mapped post hoc to solver recommendations through a strictly separated three-stage evaluation protocol. Drawing on two decades of advances in multiple kernel learning, we adopt a multi-kernel k-means formulation that jointly learns cluster assignments and kernel weights over four heterogeneous landscape views: ELA, DeepELA, DoE2Vec, and TransOptAS. On affine BBOB-derived selector tasks for Differential Evolution (DE) and Particle Swarm Optimization (PSO) at a fixed evaluation budget, we report mean plus or minus standard deviation selector profiles over 50 independent random seeds for stochastic configurations. Multi-kernel clustering obtains the strongest mean profile on the DE portfolio and remains competitive with, and nominally ahead of, the leading baselines on the more compressed PSO portfolio, where differences among the best methods are small relative to stochastic variation. In representative median-seed runs used for visualization, the learned kernel weights retain ELA and TransOptAS while assigning zero weight to DeepELA and DoE2Vec, providing a task-specific interpretation of which representations are retained by the multi-kernel model for selector-oriented grouping.

Yihang Lu, Tome Eftimov, Carola Doerr

Sorbonne Université, CNRS, INRAE, IRD, iEES, Paris, France · LIP6, Sorbonne Université, CNRS, Paris, France · Computer Systems Department, Jožef Stefan Institute, Ljubljana, Slovenia

Black-Box Optimization Unsupervised Clustering Kernel Methods

Jul 17, 2026cs.LG

Data-Native Global Optimization for Big Data K-means Clustering

Big data clustering remains challenging: the Minimum Sum-of-Squares Clustering (MSSC) problem underlying K-means is NP-hard, and existing methods either reach poor local minima or require prohibitive metaheuristic hybrids. We target arbitrarily tall data: a fixed feature space may contain arbitrarily many, possibly infinitely many, observations, while the algorithm accesses only finite random samples. We propose Big-means++, an algorithm achieving scalability and global-search quality by curating inputs to MSSC optimization on big data. It orchestrates local K-means refinements into a data-native global search for big data clustering. Rather than optimizing the full-data MSSC objective, Big-means++ traverses sample-induced surrogate landscapes. Each sample defines a distinct empirical MSSC approximation with a perturbed local-optimum structure, turning sample-to-sample variation into a global-search mechanism. Unlike Big-means, a flowing-incumbent strategy propagates centroid state across empirical landscapes through K-means refinements on fresh samples without rollback to a best-so-far solution. This increases mobility and favors stable, high-quality configurations across approximations of the full-data structure. A new shaking mechanism varies sample size geometrically, broadening the surrogate landscapes explored across resolution scales, accounting for cluster imbalance, and improving solution quality. A competitive multi-agent system asynchronously explores independent sampled landscapes, transforming diverse stochastic trajectories into collective search intelligence. Automatic convergence detection stops each agent after attaining a high-quality solution but before further search risks degrading it, while providing a universal speed-quality control. Experiments on 22 datasets against 11 competing algorithms demonstrate the effectiveness, efficiency, and robustness of Big-means++.

Ravil Mussabayev, Rustam Mussabayev, Zukhra Yerdaliyeva +1

AI Research Lab, Satbayev University, Almaty, 050000, Kazakhstan · Laboratory for Analysis and Modeling of Information Processes, Institute of Information and Computational Technologies, Almaty, 050010, Kazakhstan

Clustering Unsupervised Clustering Global Optimization

Jul 15, 2026cs.CV

A novel unsupervised machine learning strategy to handle multimodal cardiac PET/MRI data

Arrhythmogenic left ventricular cardiomyopathy is a genetic myocardial disease difficult to diagnose due to the lack of gold standard criteria. Simultaneous PET/MR imaging, combined with multiparametric quantitative analysis, could facilitate the identification of different profiles related to the phenotype and progression of cardiomyopathy. This preliminary study focuses on a methodological strategy for dealing with PET/MRI data, including inter-patient data linkage and regional analysis. Two-step clustering was applied to T1 and T2 maps, LGE, and 18F-FDG-PET images of 99 patients genetically diagnosed with arrhythmogenic left ventricular cardiomyopathy. Each patient's images were independently z-scored and summed into a single volume, which was clustered into supervoxels. Thirty-two inter-patient groups of supervoxels were obtained by spectral clustering. An "abnormality" score was assigned to each cluster and modality, and used to visualise abnormal regions likely associated with disease. They enabled the generation of automated textual and bullseye health reports for each patient, which were compared with cardiac imager assessments using balanced accuracy in repeated nested cross-validation. This approach was further validated on a larger cohort of 167 numerical phantoms. The reports generated by clustering accurately identified most of the cardiac physicians' observations (BA = 0.76

\pm

0.04 in repeated nested cross-validation on patients, and BA

\ge

0.8 on phantoms). Furthermore, the identified abnormal clusters closely matched their visual observations, facilitating the identification of varying degrees of fibrosis or inflammation on the images. This approach enables a more systematic handling of multimodal PET/MRI data to characterise myocardial heterogeneity in arrhythmogenic left ventricular cardiomyopathy patients.

Brunnhilde Ponsi, Thomas Carlier, Lara Marteau +5

Nantes Université, CHU Nantes, Nantes, France · CRCI2NA, INSERM UMR 1307, Nantes, France · Nantes Université, CHU Nantes, F-44000 Nantes, France +5

Medical Imaging Medical Image Anomaly Detection Unsupervised Clustering Multimodal Fusion

Jul 15, 2026cs.LG

FastCentNN: Accelerating Centroid Neural Network with Entropy Proxy

Centroid neural network (CentNN) is an unsupervised competitive learning algorithm in which centroid splitting is triggered only after strict local stabilization, often leading to prolonged low-movement training phases before model expansion. This report proposes FastCentNN, an accelerated variant that addresses this inefficiency by introducing an early splitting strategy based on the total centroid movement per epoch, which serves as a training entropy proxy. As a result, FastCentNN reduces unnecessary reassignment epochs while preserving the original winner-loser learning dynamics. FastCentNN supports both absolute and stage-relative movement thresholds, allowing the splitting criterion to remain either fixed or adaptive throughout training. Experiments on some benchmark datasets show that FastCentNN consistently achieves clustering quality comparable to CentNN while reducing runtime by up to 16% on synthetic 2D datasets and about 5% on high-dimensional datasets. FastCentNN therefore provides a practical and efficient drop-in replacement for CentNN, retaining its online adaptive learning behavior while offering a simple and interpretable speed-stability trade-off through configurable splitting thresholds.

Le-Anh Tran

Neural Network Optimization Unsupervised Clustering

Jul 15, 2026cs.LG

Clustering algorithms for multivariate wind farm SCADA data filtering

During wind farm operation, Supervisory Control and Data Acquisition (SCADA) systems record numerous anomalies, transients, and specific operational modes, leading to large datasets. However, for a wide range of applications, only measurements corresponding to normal operation are required and, therefore, the SCADA data must be filtered. For this purpose, several methods have been proposed to automate and replace manual filtering conducted by experts via visual inspection of the data. In this paper, we compare the filtering accuracy of multiple clustering algorithms against manual filtering, introducing evaluation metrics that are suitable for unlabeled data and robust across potential applications. Based on the results, we provide recommendations for generalizing model calibration to different datasets and discuss potential use cases for each model. The models are applied to the SCADA data of three turbines of an existing offshore wind farm, using 10-minute statistics across multiple data channels. In addition to the anomalies and operational modes typically recorded, the dataset presents a large number of non-evident outliers due to several field tests. Overall, the results highlight the importance of extending the analysis beyond the power curve, both in feature selection and in the design of evaluation metrics. In most cases, cluster-based methods are able to detect both evident and subtle outliers, achieving higher accuracy than manual filtering. However, the accuracy and the amount of data retained vary considerably depending on the model, and expert involvement remains necessary, though to a reduced extent compared to manual filtering.

Nicolò Italiano, Vasilis Pettas, Tuhfe Göçmen +1

Department of Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, Roskilde, 4000, Denmark

Unsupervised Clustering Unsupervised Anomaly Detection Time Series Clustering