the importance of orientation for the other clusters the "atypicalpoints are not very similar to the cluster centersthoughand their assignment seems somewhat arbitrary this can be attributed to the fact that -means partitions all the data points and doesn' have concept of "noisepointsas dbscan does using larger number of clustersthe algorithm could find finer distinctions howeveradding more clusters makes manual inspection even harder analyzing the faces dataset with agglomerative clustering nowlet' look at the results of agglomerative clusteringin[ ]extract clusters with ward agglomerative clustering agglomerative agglomerativeclustering(n_clusters= labels_agg agglomerative fit_predict(x_pcaprint("cluster sizes agglomerative clustering{}formatnp bincount(labels_agg))out[ ]cluster sizes agglomerative clustering[ agglomerative clustering also produces relatively equally sized clusterswith cluster sizes between and these are more uneven than those produced by -meansbut much more even than the ones produced by dbscan we can compute the ari to measure whether the two partitions of the data given by agglomerative clustering and -means are similarin[ ]print("ari{ }format(adjusted_rand_score(labels_agglabels_km))out[ ]ari an ari of only means that the two clusterings labels_agg and labels_km have little in common this is not very surprisinggiven the fact that points further away from the cluster centers seem to have little in common for -means nextwe might want to plot the dendrogram (figure - we'll limit the depth of the tree in the plotas branching down to the individual , data points would result in an unreadably dense plotclustering