k-means classifies one data point as a group
Two ideas come to mind, which could be combined or not.


Try to identify the single point as an outlier, and remove it from consideration for the clustering.
Allow $k$ to vary a little.


Using both and allowing $k\in\{2,3\}$ allows you to find only two groups in the main set of points, plus the outlier.  Using just (2) with $k\in\{3,4\}$ could find clusters Low/Med/Large/Outlier...that has the nicety that outlier detection is done by the k-means algorithm rather than another preprocessing step, but runs the risk of finding four honest clusters when you only wanted three.