[2747]{}
  15   Initial SSE         15136       15117        15118    [15116]{}       15117
       Final SSE       [14650]{}   [14650]{}    [14650]{}    [14650]{}   [14650]{}

  : \[tab\_num\_bins\_pca\] Influence of $L$ on the effectiveness of Otsu PCA-Part ($L$: \# histogram bins)

In the remaining experiments, each random method was executed a $100$ times and statistics such as *minimum*, *mean*, and *standard deviation* were collected for each performance criteria. The *minimum* and *mean* statistics represent the *best* and *average* case performance, respectively, while *standard deviation* quantifies the *variability* of performance across different runs. Note that for a deterministic method, the *minimum* and *mean* values are always identical and the *standard deviation* is always $0$.

Tables \[tab\_init\_sse\]–\[tab\_cpu\_time\] give the performance measurements for each method with respect to initial SSE, final SSE, number of iterations, and CPU time, respectively. For each of the initial SSE, final SSE, and number of iterations criteria, we calculated the ratio of the *minimum*/*mean*/*standard deviation* value obtained by each method to the best (least) *minimum*/*mean*/*standard deviation* value on each data set. These ratios will be henceforth referred to as the ‘normalized’ performance criteria. For example, on the Abalone data set (1), the *minimum* initial SSE of `F` is $424.92$ and the best *minimum* initial SSE is $21.57$ and 