thus the normalized initial SSE is about $20$. This simply means that on this data set `F` obtained approximately $20$ times worse *minimum* initial SSE than the best method. We then averaged[^3] these normalized values over the $24$ data sets to quantify the *overall* performance of each method with respect to each statistic (see Table \[tab\_summary\]). Note that we did not attempt to summarize the CPU time values in the same manner due to the sparsity of the data (see Table \[tab\_cpu\_time\]). For convenient visualization, Figure \[fig\_boxplots\] shows box plots of the normalized performance criteria. Here, the bottom and top end of the whiskers of a box represent the *minimum* and *maximum*, respectively, whereas the bottom and top of the box itself are the $25$th percentile (*Q1*) and $75$th percentile (*Q3*), respectively. The line that passes through the box is the $50$th percentile (*Q2*), i.e., the *median*, while the small square inside the box denotes the *mean*.

Discussion {#sec_disc}
==========

Best Case Performance Analysis
------------------------------

With respect to the *minimum* statistic, the following observations can be made:

-   [**Initial SSE**]{}: `OP` is the best method, followed closely by `P`, `OV`, and `V`. On the other hand, `F` is the worst 