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
Data mining algorithms in C++: data patterns and algorithms for modern applications
โ Scribed by Masters, Timothy
- Publisher
- Apress
- Year
- 2018
- Tongue
- English
- Leaves
- 296
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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 ย Read more...
Abstract: 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
โฆ Table of Contents
Content: 1. Information and Entropy --
2. Screening for Relationships --
3. Displaying Relationship Anomalies --
4. Fun With Eigenvectors --
5. Using the DATAMINE Program.
โฆ Subjects
Data mining.;Computer algorithms.;C++ (Computer program language);COMPUTERS -- General.;Computer Science.;Programming Languages, Compilers, Interpreters.;Big Data.;Data Mining and Knowledge Discovery.;Programming Techniques.;Algorithms.;Databases.;Computer programming -- software development.;Numerical analysis.;Programming & scripting languages: general.
๐ SIMILAR VOLUMES
<p>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 explanati
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
<p>Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. </p> <p>As a serious data miner you will often be faced with thousands of candidate features
The main goal of the new field of data mining is the analysis of large and complex datasets. Some very important datasets may be derived from business and industrial activities. This kind of data is known as enterprise data . The common characteristic of such datasets is that the analyst wishes to a