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 |
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`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 |

`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 |