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
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