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.
