AIMC Topic: Cluster Analysis

Clear Filters Showing 631 to 640 of 1443 articles

Feature selection for unsupervised machine learning of accelerometer data physical activity clusters - A systematic review.

Gait & posture
BACKGROUND: Identifying clusters of physical activity (PA) from accelerometer data is important to identify levels of sedentary behaviour and physical activity associated with risks of serious health conditions and time spent engaging in healthy PA. ...

Identification of disease genes and assessment of eye-related diseases caused by disease genes using JMFC and GDLNN.

Computer methods in biomechanics and biomedical engineering
Early detection of disease genes helps humans to recover from certain gene-related diseases, like genetic eye diseases. This work identifies the possibility of eye diseasesfor the disease genes utilizing a Gaussian-activation function (G)-centric dee...

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments.

Sensors (Basel, Switzerland)
The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an ...

CoSTA: unsupervised convolutional neural network learning for spatial transcriptomics analysis.

BMC bioinformatics
BACKGROUND: The rise of spatial transcriptomics technologies is leading to new insights about how gene regulation happens in a spatial context. Determining which genes are expressed in similar spatial patterns can reveal gene regulatory relationships...

Correntropy-Based Hypergraph Regularized NMF for Clustering and Feature Selection on Multi-Cancer Integrated Data.

IEEE transactions on cybernetics
Non-negative matrix factorization (NMF) has become one of the most powerful methods for clustering and feature selection. However, the performance of the traditional NMF method severely degrades when the data contain noises and outliers or the manifo...

A Novel Type-2 Fuzzy C-Means Clustering for Brain MR Image Segmentation.

IEEE transactions on cybernetics
The fuzzy C -means (FCM) clustering procedure is an unsupervised form of grouping the homogenous pixels of an image in the feature space into clusters. A brain magnetic resonance (MR) image is affected by noise and intensity inhomogeneity (IIH) durin...

Study on the semi-supervised learning-based patient similarity from heterogeneous electronic medical records.

BMC medical informatics and decision making
BACKGROUND: A new learning-based patient similarity measurement was proposed to measure patients' similarity for heterogeneous electronic medical records (EMRs) data.

An Active Learning Method Based on Variational Autoencoder and DBSCAN Clustering.

Computational intelligence and neuroscience
Active learning is aimed to sample the most informative data from the unlabeled pool, and diverse clustering methods have been applied to it. However, the distance-based clustering methods usually cannot perform well in high dimensions and even begin...

Usages of Spark Framework with Different Machine Learning Algorithms.

Computational intelligence and neuroscience
Sensors, satellites, mobile devices, social media, e-commerce, and the Internet, among others, saturate us with data. The Internet of Things, in particular, enables massive amounts of data to be generated more quickly. The Internet of Things is a ter...

Country transition index based on hierarchical clustering to predict next COVID-19 waves.

Scientific reports
COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies will face in the next years. Although the comparison between countries is essential for controlling thi...