80. Grouping unlabelled data with k-means clustering

Sometimes we may have prior knowledge that we want to group the data into a given number of clusters. Other times we may wish to investigate what may be a good number of clusters. In the example below we look at changing the number of clusters between 1 and 100 and measure the average distance points are from their closest cluster centre (kmeans.transform Looking at the results we may decide that up to about 10 clusters may be useful, but after that there are diminishing returns of adding further clusters.