
Data Mining Algorithms in C++Data Patterns and Algorithms for Modern ApplicationsBy: Timothy Masters Publication Date: 20171219 Number of pages: 304 ISBN10: 148423314X ISBN13: 9781484233146 Discover hidden relationships among the variables in your data, and learn how to exploit these relationships. This book presents a collection of datamining 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 Windowsbased DATAMINE program lets you experiment with the techniques before incorporating them into your own work. What You'll LearnUse MonteCarlo 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 energybased 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. Book preview 
ISBN Directory © 20142019 Book Search and Price Comparison