Data Mining Algorithms in C++ : Data Patterns and Algorithms for Modern Applications / by Timothy Masters

By:

Masters, Timothy [aut]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: SpringerLink Bücher | Springer eBook CollectionPublisher: Berkeley, CA : Apress, 2018Description: Online-Ressource (XIV, 286 p. 26 illus., 8 illus. in color, online resource)ISBN:

9781484233153

Subject(s):

Additional physical formats: 9781484233146 | Erscheint auch als: 978-1-4842-3314-6 Druck-AusgabeLOC classification:

QA76.7-76.73 QA76.76.C65
QA76.7-76.73
QA76.76.C65

DOI: DOI: 10.1007/978-1-4842-3315-3Online resources:

Summary: 1. Information and Entropy -- 2. Screening for Relationships -- 3. Displaying Relationship Anomalies -- 4. Fun With Eigenvectors -- 5. Using the DATAMINE Program.Summary: Find the various relationships among variables that can be present in big data as well as other data sets. This book also covers information entropy, permutation tests, combinatorics, predictor selections, and eigenvalues to give you a well-rounded view of data mining and algorithms in C++. Furthermore, Data Mining Algorithms in C++ includes classic techniques that are widely available in standard statistical packages, such as maximum likelihood factor analysis and varimax rotation. After reading and using this book, you'll come away with many code samples and routines that can be repurposed into your own data mining tools and algorithms toolbox. This will allow you to integrate these techniques in your various data and analysis projects. You will: Discover useful data mining techniques and algorithms using the C++ programming language Carry out permutation tests Work with the various relationships and screening types for these relationships Master predictor selections Use the DATAMINE program .PPN: PPN: 1658641051Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SXPC | ZDB-2-CWD

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