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Cluster Analysis

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Construction of Digital Platform of Religious and Cultural Resources Using Deep Learning and Its Big Data Analysis.

Computational intelligence and neuroscience
This article analyzes the difficulties associated with the preservation and transmission of religious cultural resources and the difficulties encountered in the new development environment and background. It does so in light of the current state of r...

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method.

Sensors (Basel, Switzerland)
The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In...

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data.

BMC bioinformatics
BACKGROUND: The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine le...

Self-supervised deep learning encodes high-resolution features of protein subcellular localization.

Nature methods
Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytose...

IoMT-Based Mitochondrial and Multifactorial Genetic Inheritance Disorder Prediction Using Machine Learning.

Computational intelligence and neuroscience
A genetic disorder is a serious disease that affects a large number of individuals around the world. There are various types of genetic illnesses, however, we focus on mitochondrial and multifactorial genetic disorders for prediction. Genetic illness...

Accurate Molecular-Orbital-Based Machine Learning Energies via Unsupervised Clustering of Chemical Space.

Journal of chemical theory and computation
We introduce an unsupervised clustering algorithm to improve training efficiency and accuracy in predicting energies using molecular-orbital-based machine learning (MOB-ML). This work determines clusters via the Gaussian mixture model (GMM) in an ent...

Kullback-Leibler Divergence-Based Fuzzy C-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation.

IEEE transactions on cybernetics
In this article, we elaborate on a Kullback-Leibler (KL) divergence-based Fuzzy C -Means (FCM) algorithm by incorporating a tight wavelet frame transform and morphological reconstruction (MR). To make membership degrees of each image pixel closer to ...

Semisupervised Affinity Matrix Learning via Dual-Channel Information Recovery.

IEEE transactions on cybernetics
This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, ...

Application of Cluster Analysis Technology in Visualization Research of Movie Review Data.

Computational intelligence and neuroscience
In order to improve the reference value of film review data, this paper combines clustering analysis technology to construct a film review clustering visualization research system to improve the visualization effect of film reviews. Moreover, this pa...

Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation.

Journal of medical Internet research
BACKGROUND: Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken to control it and keep the patient stable. Diabetes has been the subject of extensive research, rang...