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Data mining

Genstat has many conventional statistical techniques such as generalized linear models (e.g. log-linear models and logistic regression) and multivariate analysis (e.g. canonical variates analysis and cluster analysis) that are very useful for data mining. It also provides various more specialized techniques such as association rules, classification and regression trees, random forests, k-nearest-neighbours classification, self-organizing maps, neural networks and radial basis functions.

    ASRULES derives association rules from transaction data
    BCLASSIFICATION constructs a classification tree
    BCDISPLAY displays a classification tree
    BCIDENTIFY identifies specimens using a classification tree
    BCKEEP saves information from a classification tree
    BCVALUES forms values for nodes of a classification tree
    BCFOREST constructs a random classification forest
    BCFDISPLAY displays information about a random classification forest
    BCFIDENTIFY identifies specimens using a random classification forest
    BREGRESSION constructs a regression tree
    BRDISPLAY displays a regression tree
    BRKEEP saves information from a regression tree
    BRPREDICT makes predictions using a regression tree
    BRVALUES forms values for nodes of a regression tree
    BRFOREST constructs a random regression forest
    BRFDISPLAY displays information about a random regression forest
    BRFPREDICT makes predictions using a random regression forest
    KNEARESTNEIGHBOURS classifies items or predicts their responses by examining their k nearest neighbours
KNNTRAIN evaluates and optimizes the k-nearest-neighbour algorithm using cross-validation
    NNFIT fits a multi-layer perceptron neural network
    NNDISPLAY displays output from a multi-layer perceptron neural network fitted by NNFIT
    NNPREDICT forms predictions from a multi-layer perceptron neural network fitted by NNFIT
    RBFIT fits a radial basis function model
    RBDISPLAY displays output from a radial basis function model fitted by RBFIT
    RBPREDICT forms predictions from a radial basis function model fitted by RBFIT
    SOM declares a self-organizing map
    SOMADJUST performs adjustments to the weights of a self-organizing map
    SOMDESCRIBE summarizes values of variables at nodes of a self-organizing map
    SOMESTIMATE estimates the weights for self-organizing maps
    SOMIDENTIFY allocates samples to nodes of a self-organizing map
    SOMPREDICT makes predictions using a self-organizing map
    SVMFIT fits a support vector machine
    SVMPREDICT forms the predictions using a support vector machine
Updated on February 7, 2023

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