Functional clustering methods for resistance spot welding process data in the automotive industry
Sign Up to like & getrecommendations! Published in 2021 at "Applied Stochastic Models in Business and Industry"
DOI: 10.1002/asmb.2648
Abstract: Quality assessment of resistance spot welding (RSW) joints of metal sheets in the automotive industry is typically based on costly and lengthy off-line tests that are unfeasible on the full production, especially on large scale.… read more here.
Keywords: clustering methods; spot; industry; process ... See more keywords
Trajectories of loneliness across adolescence: An empirical comparison of longitudinal clustering methods using R.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of adolescence"
DOI: 10.1002/jad.12042
Abstract: INTRODUCTION In this study, we compare three different longitudinal clustering methods. As a case study, the comparison of the methods is conducted for the development of loneliness from middle childhood to young adulthood. The aim… read more here.
Keywords: clustering methods; trajectories loneliness; adolescence; longitudinal clustering ... See more keywords
Subspace Learning by $$\ell ^{0}$$ℓ0-Induced Sparsity
Sign Up to like & getrecommendations! Published in 2018 at "International Journal of Computer Vision"
DOI: 10.1007/s11263-018-1092-4
Abstract: Subspace clustering methods partition the data that lie in or close to a union of subspaces in accordance with the subspace structure. Such methods with sparsity prior, such as sparse subspace clustering (SSC) (Elhamifar and… read more here.
Keywords: ell ssc; clustering methods; subspace clustering; subspace ... See more keywords
Partitioning subjects based on high-dimensional fMRI data: comparison of several clustering methods and studying the influence of ICA data reduction in big data
Sign Up to like & getrecommendations! Published in 2019 at "Behaviormetrika"
DOI: 10.1007/s41237-019-00086-4
Abstract: In neuroscience, clustering subjects based on brain dysfunctions is a promising avenue to subtype mental disorders as it may enhance the development of a brain-based categorization system for mental disorders that transcends and is biologically… read more here.
Keywords: two step; subjects based; clustering methods; fmri ... See more keywords
Two-Phase and Graph-Based Clustering Methods for Accurate and Efficient Segmentation of Large Mass Spectrometry Images.
Sign Up to like & getrecommendations! Published in 2017 at "Analytical chemistry"
DOI: 10.1021/acs.analchem.7b01758
Abstract: Clustering is widely used in MSI to segment anatomical features and differentiate tissue types, but existing approaches are both CPU and memory-intensive, limiting their application to small, single data sets. We propose a new approach… read more here.
Keywords: graph based; clustering methods; two phase; phase graph ... See more keywords
Comparative assessment of genetic diversity matrices and clustering methods in white Guinea yam (Dioscorea rotundata) based on morphological and molecular markers
Sign Up to like & getrecommendations! Published in 2020 at "Scientific Reports"
DOI: 10.1038/s41598-020-69925-9
Abstract: Understanding the diversity and genetic relationships among and within crop germplasm is invaluable for genetic improvement. This study assessed genetic diversity in a panel of 173 D. rotundata accessions using joint analysis for 23 morphological… read more here.
Keywords: diversity matrices; diversity; clustering methods; genetic diversity ... See more keywords
Robust Spectral Clustering via Matrix Aggregation
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2871030
Abstract: Spectral clustering has become one of the most popular clustering algorithms in recent years. In real-world clustering problems, the data points for clustering may have considerable noise. To the best of our knowledge, no single… read more here.
Keywords: robust spectral; spectral clustering; matrix aggregation; clustering methods ... See more keywords
K-Clustering Methods for Investigating Social-Environmental and Natural-Environmental Features Based on Air Quality Index
Sign Up to like & getrecommendations! Published in 2020 at "IT Professional"
DOI: 10.1109/mitp.2020.2993851
Abstract: Air pollution has caused environmental and health hazards across the globe, particularly in emerging countries such as China. In this article, we propose the use of air quality index and the development of advanced data… read more here.
Keywords: quality index; air quality; air; clustering methods ... See more keywords
Comparison of methods for biological sequence clustering.
Sign Up to like & getrecommendations! Published in 2023 at "IEEE/ACM transactions on computational biology and bioinformatics"
DOI: 10.1109/tcbb.2023.3253138
Abstract: Recent advances in sequencing technology have considerably promoted genomics research by providing high-throughput sequencing economically. This great advancement has resulted in a huge amount of sequencing data. Clustering analysis is powerful to study and probes… read more here.
Keywords: clustering methods; comparison methods; sequence; sequence clustering ... See more keywords
Bayesian Adversarial Spectral Clustering With Unknown Cluster Number
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Image Processing"
DOI: 10.1109/tip.2020.3016491
Abstract: Spectral clustering is a popular tool in many unsupervised computer vision and machine learning tasks. Recently, due to the encouraging performance of deep neural networks, many conventional spectral clustering methods have been extended to the… read more here.
Keywords: clustering methods; cluster number; spectral clustering; method ... See more keywords
A comparison framework and guideline of clustering methods for mass cytometry data
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DOI: 10.1186/s13059-019-1917-7
Abstract: BackgroundWith the expanding applications of mass cytometry in medical research, a wide variety of clustering methods, both semi-supervised and unsupervised, have been developed for data analysis. Selecting the optimal clustering method can accelerate the identification… read more here.
Keywords: mass cytometry; clustering methods; cytometry data; comparison framework ... See more keywords