## Data Mining with SPSS Modeler: Theory, Exercises and SolutionsIntroducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. |

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### Contents

1 | |

25 | |

Univariate Statistics | 185 |

Multivariate Statistics | 287 |

Regression Models | 346 |

Factor Analysis | 513 |

Cluster Analysis | 587 |

Classification Models | 713 |

Using R with the Modeler | 985 |

Appendix | 1036 |

### Other editions - View all

Data Mining with SPSS Modeler: Theory, Exercises and Solutions Tilo Wendler,Sören Gröttrup No preview available - 2016 |

### Common terms and phrases

accuracy Analysis node arrow in Fig Auto Classifier node Auto Cluster node Based on dataset build calculate CHAID cross-validation Data Audit node data mining decision tree define Derive node determine dialog window discussed in section distance double-click ensemble model Euclidean distance example explained factor Figure File node Filter node final stream function Gini input variables K-Means K-Means algorithm leukemia linear regression logistic regression measure method model nugget model Stream name Model tab neural network node Fig normally distributed number of clusters option outliers output overfitting parameters Partition node prediction predictor importance Reclassify records regression model run the stream sample scale type scatterplot Sect Select node shown in Fig solution streams SPSS Modeler standard deviation stream and connect subsets SuperNode Table node target variable template stream test set Theory discussed training data Transform node TwoStep Type node value labels