Data Mining Algorithms in C++, 1st ed. Data Patterns and Algorithms for Modern Applications
Auteur : Masters Timothy
- 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
1. Information and Entropy
2. Screening for Relationships
3. Displaying Relationship Anomalies
4. Fun With Eigenvectors
5. Using the DATAMINE Program
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
Date de parution : 12-2017
Ouvrage de 286 p.
17.8x25.4 cm
Thème de Data Mining Algorithms in C++ :
Mots-clés :
Data Mining; big data; algorithms; C++; programming; mining; software; code; technique