AIMC Topic: Cluster Analysis

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EHM: Exploring dynamic alignment and hierarchical clustering in unsupervised domain adaptation via high-order moment-guided contrastive learning.

Neural networks : the official journal of the International Neural Network Society
Unsupervised domain adaptation (UDA) aims to annotate unlabeled target domain samples using transferable knowledge learned from the labeled source domain. Optimal transport (OT) is a widely adopted probability metric in transfer learning for quantify...

Fast Co-clustering via Anchor-guided Label Spreading.

Neural networks : the official journal of the International Neural Network Society
The attention towards clustering using anchor graph has grown due to its effectiveness and efficiency. As the most representative points in original data, anchors are also regarded as connecting the sample space to the label space. However, when ther...

Deep Clustering for Epileptic Seizure Detection.

IEEE transactions on bio-medical engineering
UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality and morbidity risks.

An efficient framework based on local multi-representatives and noise-robust synthetic example generation for self-labeled semi-supervised classification.

Neural networks : the official journal of the International Neural Network Society
While self-labeled methods can exploit unlabeled and labeled instances to train classifiers, they are also restricted by the labeled instance number and distribution. SEG-SSC, k-means-SSC, LC-SSC, and LCSEG-SSC are sophisticated solutions for overcom...

Interpretation of COVID-19 Epidemiological Trends in Mexico Through Wastewater Surveillance Using Simple Machine Learning Algorithms for Rapid Decision-Making.

Viruses
Detection and quantification of disease-related biomarkers in wastewater samples, denominated Wastewater-based Surveillance (WBS), has proven a valuable strategy for studying the prevalence of infectious diseases within populations in a time- and res...

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

Scientific reports
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients...

Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China.

BMC public health
BACKGROUND: Although China has implemented multiple policies to encourage childbirth, the results have been underwhelming. Migrant workers account for a considerable proportion of China's population, most of whom are of childbearing age. However, few...

A hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment.

PloS one
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performanc...

The impact of preschool children's physical fitness evaluation under self organizing maps neural network.

Scientific reports
To improve the scientific accuracy and precision of children's physical fitness evaluations, this study proposes a model that combines self-organizing maps (SOM) neural networks with cluster analysis. Existing evaluation methods often rely on traditi...