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Cluster Analysis

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Early warning strategies for corporate operational risk: A study by an improved random forest algorithm using FCM clustering.

PloS one
To enhance the accuracy and response speed of the risk early warning system, this study develops a novel early warning system that combines the Fuzzy C-Means (FCM) clustering algorithm and the Random Forest (RF) model. Firstly, based on operational r...

Compressed Representation of Extreme Learning Machine with Self-Diffusion Graph Denoising Applied for Dissecting Molecular Heterogeneity.

Journal of computational biology : a journal of computational molecular cell biology
Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profiles has been widely used to dissect molecular heterogeneity. One drawback common to most clustering ...

Semi-supervised non-negative matrix factorization with structure preserving for image clustering.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning methods have wide applications thanks to the reasonable utilization for a part of label information of data. In recent years, non-negative matrix factorization (NMF) has received considerable attention because of its interpre...

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.

JMIR formative research
BACKGROUND: Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant...

A novel data-driven approach for Personas validation in healthcare using self-supervised machine learning.

Journal of biomedical informatics
OBJECTIVE: Persona validation is a challenging task, often relying on costly external validation methods. The aim of this study was the development of a novel method for Personas validation based on data already available during their creation.

Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

Journal of geriatric oncology
INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study describes ML methods to explore and describe patient-reported symptom trajectories in older adults rece...

Deep representation learning for clustering longitudinal survival data from electronic health records.

Nature communications
Precision medicine requires accurate identification of clinically relevant patient subgroups. Electronic health records provide major opportunities for leveraging machine learning approaches to uncover novel patient subgroups. However, many existing ...

Capillariid diversity in archaeological material from the New and the Old World: clustering and artificial intelligence approaches.

Parasites & vectors
BACKGROUND: Capillariid nematode eggs have been reported in archaeological material in both the New and the Old World, mainly in Europe and South America. They have been found in various types of samples, as coprolites, sediments from latrines, pits,...

Development of a machine learning-based predictive model for maxillary sinus cysts and exploration of clustering patterns.

Head & face medicine
BACKGROUND AND OBJECTIVE: There are still many controversies about the factors influencing maxillary sinus cysts and their clinical management. This study aims to construct a prediction model of maxillary sinus cyst and explore its clustering pattern...

scMDCL: A Deep Collaborative Contrastive Learning Framework for Matched Single-Cell Multiomics Data Clustering.

Journal of chemical information and modeling
Single-cell multiomics clustering integrates multiple omics data to analyze cellular heterogeneity and is crucial for uncovering complex biological processes and disease mechanisms. However, existing matched single-cell multiomics clustering methods ...