clustering

The paper proposes a graph neural network approach to refine heuristic-based Bitcoin address clustering, releasing a dataset and introducing hierarchical clustering for better analysis of suspicious merges.

Exo labs claims to achieve 4.8 tb/s memory bandwidth through clustering Mac Studios using their RDMA solution, which could significantly boost AI inference performance. This Reddit discussion explores the implications for users considering hardware setups for AI workloads.

A tweet from @paul_cal questioning the number of clusters in a visualization, with @ArtemisConsort arguing that defining three clusters is arbitrary.

This study uses meta-clustering on milk mid-infrared spectra to identify dairy cow groups associated with negative energy balance in early lactation, revealing five distinct clusters with varying severity.

This paper presents a trilingual topic modeling framework for analyzing Sri Lankan parliamentary debates, using LLM-based text extraction and multilingual embeddings to handle code-mixed text and achieve superior cluster purity over traditional methods.

The paper shows that study-strategy clusters from EdNet logs predict learner engagement but not mastery, highlighting that behavior-only profiling is insufficient for assessing knowledge gains.

This paper presents a sociotechnical AI pipeline for ITSM ticket data, combining LLM-based schema normalization and clustering to generate executive-facing decision-support artifacts. Stakeholder evaluation shows strong ratings across interpretability, actionability, trust, and likelihood of use.

This paper introduces GRACE, a framework that uses LLM-generated semantic descriptions at the attribute-value level to create unified metric spaces for clustering mixed tabular data, achieving scalability comparable to statistical baselines while improving clustering accuracy.

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.

Introduces TRIBE, a domain-independent pipeline that uses topic modeling and clustering on team communication to predict performance early and analyze how AI agents alter team behavioral dynamics.

An analysis of Twitter's community structure using domain co-occurrence data and PCA/t-SNE, revealing tight right-wing and diffuse left-wing clusters, with implications for recommender systems and random walk mixing times.

This paper develops an ℓ0-type stability theory for subdominant (minmax) ultrametrics, proving that sparse edits propagate only through the minimum spanning tree and deriving Hamming–Lipschitz bounds on changed ultrametric entries. Experiments on deep-embedding graphs and clustering tasks demonstrate the utility of the resulting structural scores as vulnerability diagnostics.

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

The author built a pipeline that transcribes 66k podcast episodes, extracts and clusters over 700k ideas into a 2D semantic map for tracking investment narratives, and filters out AI-generated content.

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.

This paper applies clustering methods, including a Bernoulli Mixture Model, to a large dataset of UK national-level exam results to test assumptions about discrete mathematical abilities, finding that overall student ability is the dominant factor in performance.

This paper evaluates unsupervised subgrouping methods combined with causal discovery and policy evaluation for budget-constrained health interventions using observational data, finding no single method consistently outperforms others in held-out evaluation.

Proposes Big-means++, a simple algorithm that achieves global optimization quality for big data K-means clustering by systematically curating inputs and using sample-induced surrogate landscapes.

CRAFT converts rubric-based evaluation into hierarchical capability diagnosis for LLMs, identifying specific weaknesses and generating targeted fine-tuning data, achieving stronger results on finance and legal benchmarks across four open-source models.

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.