       1. K-means and K-medoids Assume we have a 2D dataset consisting of (0,-6),(4,4),(0,0),(-5,2). We wish to do k-means and k-medoids clustering with k = 2. We initialize the cluster centers with (-5,2), (0, -6). For this small dataset, in choosing between two equally valid exemplars for a cluster in k-medoids, choose them with priority in the order given above (i.e. all other things being equal, you would choose (0, -6) as a center over (-5,2)). For the following scenarios, give the clusters and cluster centers after the algorithm converges. Enter the coordinate of each cluster center as a square-bracketed list (e.g. [0, 0]); enter each cluster’s members in a similar format, separated by semicolons (e.g. [1, 2]; [3, 4]). Clustering 1 K-medoids algorithm with li norm. Cluster 1 Center: Cluster 1 Members: Cluster 2 Center: Cluster 2 Members: Clustering 2 K-medoids algorithm with 12 norm. Cluster 1 Center: Cluster 1 Members: Cluster 2 Center: Cluster 2 Members: Clustering 3 K-means algorithm with li norm Cluster 1 Center: Cluster 1 Members: Cluster 2 Center: Cluster 2 Members: