3. advanced methods

Implement convenient interfaces to, or occasionally directly implement, contemporary methods for general purpose use within R or python. Caveat: need to be robust to moderately large data. Must be accessible from R and python
* low-d embedding for visualization: isomap, tsne
* combinatorical pattern explo: correl and co-occur analysis
* unsuperv. classif: kmeans, dbscan, hdbscan, mean-shift clust, HAC
* superv. classif: c45 decision tree classifier, random forest classiifer
* encodings for categorial data: one hot, etc...