Algorithm 1 K-means clustering

1:Initialize Cluster Centroids 2:for  $\mathrm{every}\mathrm{iteration}\mathit{l}$  do 3:       Compute $r_{nk}$: 4:       for for every data point $x_n$ do 5:              Assign every data point to a cluster: 6:              for every cluster k do 7:                    if $\mathrm{k}==\mathrm{argmin}∥{\mathrm{x}}_n-{\mu }_k^{l-1}∥$ then 8:                          $r_{nk}=1$ 9:                    else 10:                          $r_{nk}=0$ 11:                    end if 12:              end for 13:       end for 14:       for every cluster k do 15:              Update cluster centroids as the mean of each cluster: 16:              ${\mu }_k^l=\frac{\sum r_{nk}{x}_n}{\sum r_{nk}}$ 17:       end for 18:end for