## 5 Evaluation in Window Analysis and Clustering Algorithms
### Evaluation in the Window Analysis Method
#### 5.1.2. Discussion in Window Analysis-FDH, BCC and CCR Models

Table 11: Window analysis for CCR model. Based on the CCR model for Figure 8:  
* The 22 nd company has the highest efficiency score with an efficiency score of 1. * The 16 th company has the second-highest efficiency score with an efficiency score of 0.994. * The 19 th company has the third-highest efficiency score with an efficiency score of 0.991. * The 6 th company has the lowest efficiency score, with an efficiency score of 0.909. Based on the CCR model for Table 12:  
* The 22 nd company has the highest efficiency score with an efficiency score of 1 for all windows. * 10 th (0.949, 0.955, 0.990, 1), 19 th (0.982, 0.995, 1, 1), and 23 rd (0.957, 0.983, 1, 1) companies have an ascending trend of efficiency from the beginning (1 st window) to the end (4 th window). * Only 11 th (0.965, 0.958, 0.948, 0.945) company has a descending trend of efficiency from the beginning (1 st window) to the end (4 th window). * Other cement companies have both descending and ascending from the beginning (1 st window) to the end (4 th window). \begin{table}
\begin{tabular}{l r r r r r r r r r r r r} \hline \hline
**Windows** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **Avg.** & **Windows** & **2014** & **2015** & **2016** & **2017** & **2018** & **2019** & **Avg.** \\ \hline
1 st window & 1.000 & 1.000 & 1.000 & & & & 1 st window & 1.000 & 0.846 & 1.000 & & & 0.948 \\
2 nd window & & 1.000 & & 1.000 & 0.978 & 0.992 & & 2 nd window & & 0.878 & 1.000 & 0.958 & & 0.943 \\
3 rd window & & & 0.822 & 0.799 & 1.000 & 0.873 & & 3 rd window & & 1.000 & 0.911 & 1.000 & 0.969 \\
4 th window & & & 0.776 & 1.000 & 1.000 & 0.925 & 4 th window & & & 0.911 & 1.000 & 1.000 & 0.969 \\ Avg. for 8 th & 1.000 & 1.000 & 0.939 & 0.850 & 1.000 & 1.000 & & Avg. for 20 th & 1.000 & 0.861 & 1.000 & 0.927 & 1.000 & 1.000 \\ \hline
1 st window & 1.000 & 1.000 & 1.000 & & & 1.000 & 1 st window & 1.000 & 0.857 & 0.874 & & & 0.908 \\
2 nd window & & 1.000 & 1.000 & 1.000 & & & 1.000 & 2 nd window & 1.000 & 1.000 & 0.945 & & 0.980 \\
3 rd window & & & 1.000 & 0.780 & 1.000 & & 0.925 & 3 rd window & & 0.746 & 0.978 & 1.000 & 0.906 \\
4 th window & & & 0.833 & 1.000 & 1.000 & 0.943 & 4 th window & & & 0.625 & 1.000 & 1.000 & 0.874 \\ Avg. for 9 th & 1.000 & 1.000 & 1.000 & 0.869 & 1.000 & 1.000 & & Avg. for 21 st & 1.000 & 0.928 & 0.871 & 0.849 & 1.000 & 1.000 \\ \hline
1 st window & 0.915 & 1.000 & 0.947 & & & 0.949 & 1 st window & 1.000 & 1.000 & 1.000 & & & 1.000 \\
2 nd window & & 1.000 & 0.948 & 0.921 & & & 0.955 & 2 nd window & & 1.000 & 1.000 & 1.000 & 1.000 \\
3 rd window & & & 0.974 & 1.000 & 1.000 & & 0.990 & 3 rd window & & 1.000 & 1.000 & 1.000 & 1.000 \\
4 th window & & & 1.000 & 1.000 & 1.000 & 1.000 & 4 th window & & 1.000 & 1.000 & 1.000 & 1.000 \\ Avg. for 10 th & 9.15 & 1.000 & 0.957 & 0.973 & 1.000 & 1.000 & & Avg. for 22 nd & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline
1 st window & 1.000 & 0.899 & 1.000 & & & & 0.965 & 1 st window & 0.922 & 0.955 & 1.000 & & & 0.957 \\
2 nd window & & 1.000 & 1.000 & 0.875 & & & 0.958 & 2 nd window & & 0.955 & 1.000 & 1.000 & & 0.983 \\
3 rd window & & & 1.000 & 0.845 & 1.000 & & 0.948 & 3 rd window & & & 1.000 & 1.000 & 1.000 & 1.000 \\
4 th window & & & & 0.835 & 1.000 & 1.000 & 0.945 & 4 th window & & & 1.000 & 1.000 & 1.000 & 1.000 \\ Avg. for 11 th & 1.000 & 0.949 & 1.000 & 0.851 & 1.000 & 1.000 & & Avg. for 23 rd & 0.922 & 0.955 & 1.000 & 1.000 & 1.000 & 1.000 \\ \hline
1 st window & 1.000 & 1.000 & 0.958 & & & 0.985 & 1 st window & 1.000 & 0.857 & 0.873 & & & 0.908 \\
2 nd window & & 1.000 & 1.000 & 0.939 & & & 0.979 & 2 nd window & & 1.000 & 1.000 & 0.945 & & 0.980 \\
3 rd window & & & 1.000 & 1.000 & 1.000 & & 3 rd window & & & 0.684 & 0.921 & 1.000 & & 0.868 \\
4 th window & & & & 1.000 & 1.000 & 1.000 & 1.000 & 4 th window & & & 0.876 & 1.000 & 1.000 & 0.958 \\ Avg. for 12 th & 1.000 & 1.000 & 0.985 & 0.978 & 1.000 & 1.000 & & Avg. for 24 th & 1.000 & 0.928 & 0.850 & 0.914 & 1.000 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table}