It can be seen that, with respect to initial SSE and number of iterations criteria, `OV` outperforms `V` more often than not. On the other hand, `OP` frequently outperforms `P` with respect to both criteria. As for final SSE, `OP` performs slightly better than `P`, whereas `OV` performs slightly worse than `V`. It appears that Otsu’s method benefits `P` more than it benefits `V`. This is most likely due to the fact that histograms of projections over the major axis necessarily have a greater dynamic range and variability and thus are more amenable to thresholding compared to histograms of projections over any coordinate axis.

Recommendations for Practitioners
---------------------------------

Based on the analyses presented above, the following recommendations can be made:

-   In general, `X` should not be used. As mentioned in Section \[sec\_init\_methods\], this method was not designed specifically as a k-means initializer [@Gonzalez85]. It is easy to understand and implement, but is mostly ineffective and unreliable. Furthermore, despite its low overhead, this method does not offer significant time savings since it often results in slow k-means convergence.

-   In applications that involve small data sets, e.g., $N < 1,000$, `K` should be used. It is computationally feasible to run 