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?
Edit: If I replace the normalisation factor / data.shape with squared **2 as suggested below, then I still don't get the same as for the inertia plot:
distortions_2.append(sum(np.min(cdist(data, kmeanModel.cluster_centers_, 'euclidean'), axis = 1)) ** 2)
Using squared just makes the plot a little smoother, but definitely not the same as using intertia_, I'm just not quite sure how inertia_ is calculated and what it means.