unsupervised-learning

The paper presents an unsupervised method to extract linguistic metaphors and group them into conceptual metaphors, applying the approach to analyze framing differences in left- vs. right-leaning podcasts.

This paper introduces LUCID, an LLM-guided interpretable and training-free unsupervised community detection method that uses a four-stage pipeline inspired by phase-transition kinetics to achieve state-of-the-art performance on real-world graphs.

freeCodeCamp published a comprehensive handbook on clustering in Python, covering K-means, hierarchical, and DBSCAN clustering with implementations and visualizations.

This paper proposes RHEA, a reliability-aware framework for multimodal-attributed graph clustering that estimates node-specific modality reliability from neighborhood consensus, reconstructs unreliable modalities, and uses reliability-aware fusion and optimal transport clustering. Experiments on four benchmarks show consistent gains, especially under noisy or missing attributes.

This paper investigates deep clustering methods on imbalanced tabular data and proposes two novel ensemble approaches that aggregate clustering assignments across embedding dimensions or via majority voting, outperforming individual methods on 16 datasets.

This paper presents a novel unsupervised data augmentation method combining Gaussian Mixture Models and Large Language Models to improve clustering on imbalanced text datasets by generating synthetic documents for underrepresented clusters.

ConvGRUAutoencoder combines convolutional layers with gated recurrent units to compress and reconstruct video sequences, enabling unsupervised learning from video without human labeling.

Proposes Hybrid Latent-Structural Fusion (HLSF), a weighted anomaly fusion framework combining CP-APR structural anomaly scores with latent-space density scores from normalizing flows, improving cyber anomaly detection on real-world compromised user credentials data.

Latent actions are gaining traction in robotics as a way to learn from unlabeled video without action labels. Recent papers from DeepMind and FAIR demonstrate progress from controlled game environments to in-the-wild internet video, promising scalable training for imitation learning.

FastCentNN is an accelerated variant of the Centroid Neural Network that uses an entropy proxy based on centroid movement per epoch to trigger early splitting, reducing runtime by up to 16% while maintaining clustering quality.

This paper compares clustering algorithms for filtering wind farm SCADA data to retain only normal operation measurements, introducing evaluation metrics for unlabeled data and providing recommendations based on tests on three offshore turbines.

Presents Oat, a lightweight unsupervised method for identifying error steps in LLM-based agentic failure trajectories using neural controlled differential equations trained only on successful trajectories. It achieves 200-5000x speedup over prompting baselines with significant F1 improvements in in-domain and out-of-distribution settings.

This paper proposes C2TSP, an end-to-end unsupervised learning method for the Traveling Salesman Problem that learns a tractable distribution over near-tour structures using a connected-by-construction Gibbs family, incorporating implicit differentiation and certificate-guided sharpening to preserve interpretable Hamiltonian structure.

This paper demonstrates that unsupervised visual pretraining on documents, without text extraction, consistently outperforms text-only pretraining for language intelligence, providing an efficient and scalable approach for foundation models.

Proposes a novel self-supervised image clustering framework that uses an evolution-strategy outer loop to maximize a 'surprise score' without needing a per-step loss, paired with a gradient-descent inner loop, achieving state-of-the-art results on standard benchmarks in the strict non-parametric setting.

Proposes TENSOR, an unsupervised anomaly detection method for identifying information operations users by analyzing temporal behavioral and language patterns using temporal point processes and LLM responses. Outperforms baselines on five real-world datasets.

This paper proposes SCISE, a scalable unsupervised graph clustering framework that uses community-aware sampling and structural entropy to overcome structural isolation in mini-batch training, achieving state-of-the-art results on benchmark datasets.

Proposes a novel distance metric for intra-attribute distances in categorical data clustering that treats nominal and ordinal attributes separately while preserving order relationships, and introduces a clustering algorithm that integrates weight learning and partitioning.

This paper presents an unsupervised clustering-based framework using K-Means++ to detect suspicious trading patterns in capital market data, achieving a silhouette score of 0.561 and identifying 2.02% of trades as potentially fraudulent.

Proposes a novel framework combining active learning with masked reconstruction and minimax strategies to improve unsupervised time series anomaly detection, achieving 12.39% AUC improvement over baselines across 28 test cases.