A common step when clustering using k-means is to first standardize the dataset so that each feature has zero mean and unit variance.
I understand why forcing unit variance helps k-means generate better clusterings. Leaving the different variables in unstandardized form effectively puts more weight on the variables with higher variance. However, I'm less sure about the reasoning behind the step of subtracting off the mean before k-means. How does this affect the clusterings generated by k-means?