K-Means anomaly detection not clustering anomalies
By multiplying the maximum by 10 in a loop, you are repeatedly multiplying by $\sim 10$, so that the final point is $\sim 10^{53}$ (hrm, the plot actually doesn't go quite that far?).  Hence the last added point is so far away from the rest, it becomes too costly to include any other points in that cluster.  (Doing so drags the mean of the cluster very far away, and then the cost function blows up; see the sklearn manual.)