Acceptable SSE (sum of squared errors) for K-means

Question

I am developing a k-means clustering algorithm, and I have obtained the ideal number of clusters based on the elbow method. However, despite the fact that the error diminishes a lot with the number of clusters, when it stabilizes the SSE value is still in the order of 2x10^+6. Is this acceptable? I can't seem to find any information on this.

Thank you in advance!

Answer 1

There is no benchmark for an acceptable SSE.

Assume your data are points located in two-dimensional space. If you measure distances in millimeters, in meters or in kilometers will change the SSE by factors of $10^6$, regardless of the clustering.

What is "acceptable" will depend on your problem, your data and alternatives to the clustering you have chosen.