AIMC Topic: Cluster Analysis

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[Cluster predictors of trajectories of leisure-time physical activity intensity in men and women from ELSA-Brasil].

Cadernos de saude publica May 19, 2025
The maintenance of physical activity over time is a challenge for public health. Predictors of different physical activity intensities have not been sufficiently analyzed. This study aimed to identify clusters of trajectories of physical activity int...
Female Adult Male Humans Surveys and Questionnaires Leisure Activities Socioeconomic Factors Middle Aged Brazil Exercise Sex Factors Longitudinal Studies Cluster Analysis
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Learning to solve combinatorial optimization problems with heterophily.

Neural networks : the official journal of the International Neural Network Society May 7, 2025
Graph Neural Networks (GNNs) are widely used to address combinatorial optimization problems. However, many popular GNNs struggle to generalize to heterophilic scenarios where adjacent nodes tend to be with different labels or dissimilar features, suc...
Cluster Analysis Humans Neural Networks, Computer Machine Learning Algorithms
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Pattern recognition in crash clusters involving vehicles with advanced driving technologies.

Accident; analysis and prevention May 6, 2025
Autonomous Vehicle (AV) technologies, including Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS), have significant potential to reduce crashes caused by driver errors. However, as AVs become more prevalent on roadways, th...
Automobiles Accidents, Traffic Automobile Driving Humans Risk Factors Cluster Analysis Automation
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Analyzing and predicting global happiness index via integrated multilayer clustering and machine learning models.

PloS one Apr 30, 2025
This study addresses the research objective of predicting global happiness and identifying its key drivers. We propose a novel predictive framework that integrates unsupervised and supervised machine learning techniques to uncover the complex pattern...
Social Support Humans Cluster Analysis Machine Learning Happiness
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Interpretable unsupervised neural network structure for data clustering via differentiable reconstruction of ONMF and sparse autoencoder.

Neural networks : the official journal of the International Neural Network Society Apr 29, 2025
Neural networks, while powerful, often face significant challenges in terms of interpretability, particularly in clustering tasks. Traditional methods typically rely on post-hoc explanations or supervised learning, which limit their ability to provid...
Cluster Analysis Neural Networks, Computer Unsupervised Machine Learning Algorithms Autoencoder
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GINClus: RNA structural motif clustering using graph isomorphism network.

NAR genomics and bioinformatics Apr 26, 2025
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming...
Deep Learning Cluster Analysis RNA Nucleic Acid Conformation Nucleotide Motifs Software Algorithms
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Identification of Risk Group for Root Caries and Analysis of Associated Factors in Older Adults Using Unsupervised Machine Learning Clustering.

Clinical interventions in aging Apr 24, 2025
PURPOSE: This study aimed to identify the high-risk group for root caries using unsupervised machine learning and to explore the associated factors.
ROC Curve Logistic Models Age Factors Male Risk Assessment Cluster Analysis Unsupervised Machine Learning Risk Factors Root Caries Humans Aged Female Cross-Sectional Studies
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LACE-UP: An ensemble machine-learning method for health subtype classification on multidimensional binary data.

Proceedings of the National Academy of Sciences of the United States of America Apr 23, 2025
Disease and behavior subtype identification is of significant interest in biomedical research. However, in many settings, subtype discovery is limited by a lack of robust statistical clustering methods appropriate for binary data. Here, we introduce ...
Cluster Analysis Principal Component Analysis Cardiovascular Diseases Latent Class Analysis Humans Machine Learning Algorithms
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Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

Scientific reports Apr 18, 2025
This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEGF) treatment outcomes using an unsupervised machine learning (ML) approach based on radiomic feature...
Diabetic Retinopathy Retrospective Studies Female Aged Radiomics Male Humans Visual Acuity Treatment Outcome Macular Edema Principal Component Analysis Middle Aged Cluster Analysis Vascular Endothelial Growth Factor A Angiogenesis Inhibitors Tomography, Optical Coherence Unsupervised Machine Learning
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BL-FlowSOM: Consistent and Highly Accelerated FlowSOM Based on Parallelized Batch Learning.

Cytometry. Part A : the journal of the International Society for Analytical Cytology Apr 17, 2025
The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and sp...
Principal Component Analysis Flow Cytometry Algorithms Computational Biology Cluster Analysis Machine Learning Software Humans
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