Random forest
Like our motivation with the use of the Gower metric in handling mixed, in fact, messy data, we can apply random forest in an unsupervised fashion. Selection of this method has some advantages:
- Robust against outliers and highly skewed variables
- No need to transform or scale the data
- Handles mixed data (numeric and factors)
- Can accommodate missing data
- Can be used on data with a large number of variables, in fact, it can be used to eliminate useless features by examining variable importance
- The dissimilarity matrix produced serves as an input to the other techniques discussed earlier (hierarchical, k-means, and PAM)
A couple words of caution. It may take some trial and error to properly tune the Random Forest with respect to the number of variables sampled at each tree split (mtry = ? in the function) and the number of trees grown. Studies done show that the more trees grown, up to a point, provide better results, and a good starting point is to grow 2,000 trees (Shi, T. &...
