- Data Understanding (semantics, descriptive statistics, quality assessment, variable transformation, pairwise correlation and elimination of redundant variables)
- Clustering Analysis (K-means, density-based, hierarchical)
- Association Rule Mining
- Classification via Decision Trees (model interpretation and validation with different gain formulae) and via K-Nearest Neighbors
