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“The most famous problem of clustering is that, in some dimensions, we need a lot of data to do it. Clustering is a powerful method for unsupervised learning because the number of clusters can be large and the resulting model is well-conditioned. But when you have too many data points, you get “noise” that can break the methodology: the number of clusters becomes lower than the number of data points, and it becomes inefficient to perform the clustering. But when you have the opposite: too many data points, clustering becomes inefficient to