Climate regionalization in Bolivia: A combination of non‐hierarchical and consensus clustering analyses based on precipitation and temperature
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Climatology"
DOI: 10.1002/joc.6464
Abstract: Climate regionalization is an inseparable part of many climate change and environmental studies. Delineating climatologically homogeneous regions enhances the utility of such studies and reduces the biases due to the uncertainties associated with climate model… read more here.
Keywords: climate; consensus clustering; bolivia; climate regionalization ... See more keywords
SC3 - consensus clustering of single-cell RNA-Seq data
Sign Up to like & getrecommendations! Published in 2017 at "Nature methods"
DOI: 10.1038/nmeth.4236
Abstract: Single-cell RNA-seq enables the quantitative characterization of cell types based on global transcriptome profiles. We present single-cell consensus clustering (SC3), a user-friendly tool for unsupervised clustering, which achieves high accuracy and robustness by combining multiple… read more here.
Keywords: single cell; consensus clustering; cell; cell rna ... See more keywords
Consensus Clustering of temporal profiles for the identification of metabolic markers of pre-diabetes in childhood (EarlyBird 73)
Sign Up to like & getrecommendations! Published in 2017 at "Scientific Reports"
DOI: 10.1038/s41598-017-19059-2
Abstract: In longitudinal clinical studies, methodologies available for the analysis of multivariate data with multivariate methods are relatively limited. Here, we present Consensus Clustering (CClust) a new computational method based on clustering of time profiles and… read more here.
Keywords: clustering temporal; profiles identification; temporal profiles; identification metabolic ... See more keywords
GMHCC: High-throughput Analysis of Biomolecular Data using Graph-based Multiple Hierarchical Consensus Clustering.
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac290
Abstract: MOTIVATION Thanks to the development of high-throughput sequencing technologies, massive amounts of various biomolecular data have been accumulated to revolutionize the study of genomics and molecular biology. One of the main challenges in analyzing this… read more here.
Keywords: consensus clustering; graph based; multiple hierarchical; biomolecular data ... See more keywords
An Improved Consensus Clustering Algorithm Based on Cell-Like P Systems With Multi-Catalysts
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3010475
Abstract: Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such… read more here.
Keywords: tex math; consensus; clustering algorithm; consensus clustering ... See more keywords
Qualitative Clustering of Software Repositories Based on Software Metrics
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3244495
Abstract: Software repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository… read more here.
Keywords: software; software metrics; software repositories; qualitative clustering ... See more keywords
Accuracy Improvement of Non-Intrusive Load Monitoring Using Voting-Based Consensus Clustering
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Access"
DOI: 10.1109/access.2023.3279489
Abstract: Widespread inculcation of smart meter data in modern grid is motivating stakeholders to utilize it for demand response management and achieving energy sustainability goals. One of the methods being used in this regard is Non-Intrusive… read more here.
Keywords: intrusive load; voting based; based consensus; non intrusive ... See more keywords
Successive Consensus Clustering for Unsupervised Video-Based Person Re-Identification
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3156443
Abstract: Person re-identification is to match the same person between non-overlapping cameras. This paper focuses on unsupervised video-based person re-identification. The mainstream approach is to obtain pseudo-labels by clustering samples for training the classification model. In… read more here.
Keywords: person; person identification; unsupervised video; consensus clustering ... See more keywords
Heatmaps and consensus clustering for ego network exploration
Sign Up to like & getrecommendations! Published in 2022 at "F1000Research"
DOI: 10.12688/f1000research.108964.1
Abstract: Background: Researchers need visualization methods (using statistical and interactive techniques) to efficiently perform quality assessments and glean insights from their data. Data on networks can particularly benefit from more advanced techniques since typical visualization methods,… read more here.
Keywords: consensus clustering; network; network data; heatmaps consensus ... See more keywords
Voting-based Approach in Consensus Clustering through q-fold cross-validation
Sign Up to like & getrecommendations! Published in 2019 at "Electronic Journal of Applied Statistical Analysis"
DOI: 10.1285/i20705948v12n3p657
Abstract: Over the past 50 years, extensive research have been carried out to understand how clustering work in classifying data into meaningful groups. Various clustering algorithms and cluster validity indexes have been proposedand improvised to obtain… read more here.
Keywords: consensus; approach; consensus clustering; fold cross ... See more keywords
Use of Machine Learning Consensus Clustering to Identify Distinct Subtypes of Kidney Transplant Recipients With DGF and Associated Outcomes
Sign Up to like & getrecommendations! Published in 2022 at "Transplant International"
DOI: 10.3389/ti.2022.10810
Abstract: Data and transplant community opinion on delayed graft function (DGF), and its impact on outcomes, remains varied. An unsupervised machine learning consensus clustering approach was applied to categorize the clinical phenotypes of kidney transplant (KT)… read more here.
Keywords: learning consensus; machine learning; kidney transplant; transplant recipients ... See more keywords