tive clusteringwith numbered data points (cf figure - while this visualization provides very detailed view of the hierarchical clusteringit relies on the two-dimensional nature of the data and therefore cannot be used on datasets that have more than two features there ishoweveranother tool to visualize hierarchical clusteringcalled dendrogramthat can handle multidimensional datasets unfortunatelyscikit-learn currently does not have the functionality to draw dendrograms howeveryou can generate them easily using scipy the scipy clustering algorithms have slightly different interface to the scikit-learn clustering algorithms scipy provides function that takes data array and computes linkage arraywhich encodes hierarchical cluster similarities we can then feed this linkage array into the scipy dendrogram function to plot the dendrogram (figure - )in[ ]import the dendrogram function and the ward clustering function from scipy from scipy cluster hierarchy import dendrogramward xy make_blobs(random_state= n_samples= apply the ward clustering to the data array the scipy ward function returns an array that specifies the distances bridged when performing agglomerative clustering linkage_array ward(xclustering