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clustering method based on splitting and merging of gaussian mixture models  (MathWorks Inc)


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    MathWorks Inc clustering method based on splitting and merging of gaussian mixture models
    Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.
    Clustering Method Based On Splitting And Merging Of Gaussian Mixture Models, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/clustering method based on splitting and merging of gaussian mixture models/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    clustering method based on splitting and merging of gaussian mixture models - by Bioz Stars, 2026-05
    90/100 stars

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    1) Product Images from "From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies"

    Article Title: From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

    Journal: Vaccines

    doi: 10.3390/vaccines8010138

    Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.
    Figure Legend Snippet: Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.

    Techniques Used: Software, Flow Cytometry, Control, Construct, Biomarker Discovery, Sampling, Diffusion-based Assay, Preserving

    Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.
    Figure Legend Snippet: Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.

    Techniques Used: Generated, Standard Deviation, Transformation Assay



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    clustering method based on splitting and merging of gaussian mixture models  (MathWorks Inc)
    90
    MathWorks Inc clustering method based on splitting and merging of gaussian mixture models
    Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.
    Clustering Method Based On Splitting And Merging Of Gaussian Mixture Models, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/clustering method based on splitting and merging of gaussian mixture models/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    clustering method based on splitting and merging of gaussian mixture models - by Bioz Stars, 2026-05
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.

    Journal: Vaccines

    Article Title: From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

    doi: 10.3390/vaccines8010138

    Figure Lengend Snippet: Automated tools for the analysis of cytometric data. For each tool is reported the function, the statistical platform in which they are available, and a brief description of the main function.

    Article Snippet: , SWIFT , Matlab , Clustering method based on splitting and merging of Gaussian mixture models , [ ] .

    Techniques: Software, Flow Cytometry, Control, Construct, Biomarker Discovery, Sampling, Diffusion-based Assay, Preserving

    Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.

    Journal: Vaccines

    Article Title: From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

    doi: 10.3390/vaccines8010138

    Figure Lengend Snippet: Data transformations. ( a ) A random data set was generated using two gaussian distributions, centered at 10 2 (10000 cells) and 2*10 3 (1000 cells), and with standard deviation equal to 10 2 and 10 3 , respectively. The probability histogram is shown on a linear scale. ( b ) Arcsinh transformation. The parameter λ defines both the width of the linear region, and its slope. The transforming function is approximately linear for λ close to zero, while it approaches logarithmic transformations when x >> λ or when x << −λ. ( c ) Logicle transformation. The shape of the transforming function is defined by the parameters M, A, and W, which can be intuitively interpreted respectively as the number of decades, the number of negative decades, and the width of the linear region. ( d ) Probability histogram of the data in a transformed with the arcsinh function. ( e ) Probability histogram of the data in a transformed with the logicle function.

    Article Snippet: , SWIFT , Matlab , Clustering method based on splitting and merging of Gaussian mixture models , [ ] .

    Techniques: Generated, Standard Deviation, Transformation Assay