You could benchmark an implementation by speed.

If you have known clusters, for example generating data from three distributions or real world data like the Iris dataset, you can measure how well the clustering works.

There are also a variety of metrics that give insight into performance, or at least performance when compared to other algorithms.

But if you are asking how do we know this is a "real" cluster, then we can't. We can only say for sure that for a dataset with n observations, there is n possible clusters.