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Data Mining with SPSS Modeler Theory, Exercises and Solutions

Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer program...

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Bibliographic Details
Main Authors: Wendler, Tilo, Gröttrup, Sören (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2021, 2021
Edition:2nd ed. 2021
Subjects:
Statistical Theory And Methods
Statistics 
Statistics And Computing/statistics Programs
Data Mining
Data Mining And Knowledge Discovery
Computer Software
Mathematical Software
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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505 0 |a Preface -- Introduction -- Basic Functions of the SPSS Modeler -- Univariate Statistics -- Multivariate Statistics -- Regression Models -- Factor Analysis -- Cluster Analysis -- Classification Models -- Using R with the Modeler -- Imbalanced Data and Resampling Techniques -- Case Study: Fault Detection in Semiconductor Manufacturing Process -- Appendix 
653 |a Statistical Theory and Methods 
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653 |a Statistics and Computing/Statistics Programs 
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653 |a Data Mining and Knowledge Discovery 
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653 |a Mathematical Software 
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520 |a Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. 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. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining 

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