Data Mining Algorithms in C++

Timothy Masters

ISBN 10: 148423314X / ISBN 13: 9781484233146

Edité par Apress, Apress Dez 2017, 2017

A propos de cet article

Neuware -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.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 304 pp. Englisch.

N° de réf. du vendeur 9781484233146

Détails bibliographiques

• Titre: Data Mining Algorithms in C++
• Éditeur: Apress, Apress Dez 2017
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 148423314X
• ISBN à 13 chiffres: 9781484233146
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

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.

À propos de l?auteur

Timothy Masters has a PhD in statistics and is an experienced programmer. His dissertation was in image analysis. His career moved in the direction of signal processing, and for the last 25 years he's been involved in the development of automated trading systems in various financial markets.