Data Mining Algorithms in C++

Timothy Masters

ISBN 10: 148423314X / ISBN 13: 9781484233146

Published by Apress, 2017

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. An expert-driven data mining and algorithms in C++ bookData mining is an important topic in big dataAlgorithms are also a critical topic of growing importance&nbspTimothy Masters h. Seller Inventory # 174254460

Bibliographic Details

Title: Data Mining Algorithms in C++

Publisher: Apress

Publication Date: 2017

Binding: Soft cover

Condition: New

About this title

Synopsis:

Discover hidden relationships among the variables in your data, and learn how to exploit these relationships.  This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications.  All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.
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.

From the Back Cover:

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