Overview
- An expert-driven data mining and algorithms in C++ book
- Data mining is an important topic in big data
- Algorithms are also a critical topic of growing importance
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About this book
Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program. The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.
What You'll Learn
- Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data
- Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data
- Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods
- See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data
- Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high
Who This Book Is For
Anyone interested in discovering and exploiting relationships among variables. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.
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Table of contents (5 chapters)
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Bibliographic Information
Book Title: Data Mining Algorithms in C++
Book Subtitle: Data Patterns and Algorithms for Modern Applications
Authors: Timothy Masters
DOI: https://doi.org/10.1007/978-1-4842-3315-3
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Timothy Masters 2018
Softcover ISBN: 978-1-4842-3314-6Published: 19 December 2017
eBook ISBN: 978-1-4842-3315-3Published: 15 December 2017
Edition Number: 1
Number of Pages: XIV, 286
Topics: Programming Languages, Compilers, Interpreters, Big Data, Data Mining and Knowledge Discovery, Programming Techniques, Algorithms