Algorithm 1.

sidClustering

1: procedure sidClustering(${\left({\mathbf{X}}_i\right)}_{i=1}^n$, δ)

2:  Sidify the original variables using Algorithm 2

3:  Use SID interaction features to predict SID main features using MVRF

4:  Extract the random forest distance from the trained multivariate forest

5:  Calculate Euclidean distance on the matrix of distances

6:  Cluster the observations based on distance of Step 5 utilizing HC or PAM

7: end procedure