I am trying to visualise clusters in a dendrogram. I would usually use
scipy.cluster.hierarchy.dendrogram to visualise them, but since I've created those clusters with my own logic (below), I am having trouble to visualise clustered dimensions.
I've tried to use
dendrogram(link_color_func=...) to assign the same link colour to dimensions of the same cluster, but I am struggling to convert the index passed into
link_color_func to the clusters (
dim_clusters). Perhaps there's even a better way to visualise those clusters.
# x.shape = (num dimensions, num observations) z = hac.linkage(x.T, method='complete', metric='correlation') cluster_ids = hac.fcluster(z, 1.0, criterion='distance') # Try different clustering to limit number of clusters if cluster_ids.max() > max_clusters: cluster_ids = hac.fcluster(z, max_clusters, criterion='maxclust') # Get original dimension's indices. for c in np.unique(cluster_ids): ixs = np.where(cluster_ids == c).tolist() dim_clusters.append(ixs)