What a great article. Great visualizations, and I particularly like the focus on post clustering analysis. This doesn't get addressed enough in undergraduate/ g

What a great article. Great visualizations, and I particularly like the focus on post clustering analysis. This doesn't get addressed enough in undergraduate/ graduate level training on clustering. I will raise one 'issue', that I expected to see in the first section regarding k-means, and clustering in general, which is the issue of variable importance/ variable selection. I didn't see anything addressing normalization of values, how to deal with factor variables, or variable weighting. Clustering for data science is something that I engage in daily and while its a bit of a detail, how you normalize (if you do so), and how you weight variables is the key to getting a useful set of clusters or not. Maybe its an aside, but maybe step through the k-means algorithm early on and show why normalization and weights matter. Just a suggestion. Edit: I suppose this is addressed through PCA. There are ways of addressing these issues outside of PCA.