I've found two different approaches online when using the Elbow Method to determine the optimal number of clusters for K-Means.
One approach is to use the following code:
distortions_2.append(sum(np.min(cdist(data , kmeanModel.cluster_centers_ , 'euclidean') , axis = 1)) / data.shape)
Another is to use inertia_ from sklearn.cluster.KMeans:
When I plot the results (using the same random states) both give different results but I'm not sure what the differences are, can anyone help?