Data Mining with SPSS Modeler

Name: Data Mining with SPSS Modeler
Brand: Springer Nature Switzerland AG
Price: 1275 SEK
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Theory, Exercises and Solutions

AvTilo Wendler,Sören Gröttrup

Springer Nature Switzerland AG

2022

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Beskrivning

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.

Produktinformation

Märke:Springer Nature Switzerland AG
Utgivningsdatum:2022-05-26
Höjd:155 x 235 x 52 mm
Vikt:2 194 g
Språk:Engelska
Antal sidor:1 274
Upplaga:2
Förlag:Springer Nature Switzerland AG
EAN:9783030543396
Originaltitel:Data Mining with SPSS Modeler: Theory, Exercises and Solutions

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik
Databaser inom Data och IT

Mer om författaren

Dr. Tilo Wendler is a Professor at the University of Applied Sciences HTW Berlin, Germany. He studied mathematics, physics and business information technology. In his doctoral thesis, he examined determinants of user expectations in using information technology. He is also interested in applying complex statistical methods in the banking sector, especially in the field of rating methods. He has been teaching business statistics and data mining for ten years.Dr. Sören Gröttrup is a Professor of Machine Learning and Statistics at the Technische Hochschule Ingolstadt, Germany. After studying mathematics and computer science, he was awarded a Ph.D. for his research on stochastic models with biological applications. Alongside his doctoral studies, he worked as a data analyst, analyzing genomic data at a research institute. For many years, he worked as a data scientist in the airline and data-driven marketing business, and advised various companies on digitalization and machine learning projects.

Innehållsförteckning

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.