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Data Mining Algorithms in C++
Data Patterns and Algorithms for Modern Applications
Taschenbuch von Timothy Masters
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
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.
Zusammenfassung

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

Inhaltsverzeichnis
1. Information and Entropy.- 2. Screening for Relationships.- 3. Displaying Relationship Anomalies.- 4. Fun With Eigenvectors.- 5. Using the DATAMINE Program.

Details
Erscheinungsjahr: 2017
Fachbereich: Programmiersprachen
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 304
Inhalt: xiv
286 S.
ISBN-13: 9781484233146
ISBN-10: 148423314X
Sprache: Englisch
Herstellernummer: 978-1-4842-3314-6
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Masters, Timothy
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 17 mm
Von/Mit: Timothy Masters
Erscheinungsdatum: 19.12.2017
Gewicht: 0,576 kg
preigu-id: 111022919
Über den Autor
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.
Zusammenfassung

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

Inhaltsverzeichnis
1. Information and Entropy.- 2. Screening for Relationships.- 3. Displaying Relationship Anomalies.- 4. Fun With Eigenvectors.- 5. Using the DATAMINE Program.

Details
Erscheinungsjahr: 2017
Fachbereich: Programmiersprachen
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Seiten: 304
Inhalt: xiv
286 S.
ISBN-13: 9781484233146
ISBN-10: 148423314X
Sprache: Englisch
Herstellernummer: 978-1-4842-3314-6
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Masters, Timothy
Auflage: 1st ed.
Hersteller: Apress
Apress L.P.
Maße: 254 x 178 x 17 mm
Von/Mit: Timothy Masters
Erscheinungsdatum: 19.12.2017
Gewicht: 0,576 kg
preigu-id: 111022919
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