method, followed by `X`. These two methods give $2$–$3$ times worse *minimum* initial SSE than the best method. It can be seen that multiple runs of random methods do not produce good initial clusterings. In contrast, only a single run of `OP`, `P`, `OV`, or `V` often gives very good results. This is because these methods are approximate clustering algorithms by themselves and thus they give reasonable results even without k-means refinement.

-   [**Final SSE**]{}: `X` is the worst method, while the remaining methods exhibit very similar performance. This homogeneity in performance is because k-means can take two disparate initial configurations to similar (or even identical) local minima. Given the abundance of local minima even in data sets of moderate size and/or dimensionality and the gradient descent nature of k-means, it is not surprising that the deterministic methods (except `X`) perform slightly worse than the random methods as the former methods were executed only once, whereas the latter ones were executed a $100$ times.

-   [**Number of Iterations**]{}: `K` is the best method, followed by `M` and `F`. `X` is the worst method. As in the case of final SSE, random methods outperform deterministic methods due to their multiple-run advantage.

