## 5 Experimental Studies and Tests
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Acc.** & **CSE** & **Mar.** & **MI** & **Coo.** \\ \hline DQ & - & - & - & - & - \\ SOM & 737 & 307 & 278 & 142 & 16 \\ KMeans++ & 736 & 307 & 278 & 142 & 18 \\ BIRCH & 29 & 10 & 9 & 8 & 6 \\ S-CF Tree(2) & 20 & 16 & 16 & 16 & 8 \\ S-CF Tree(3) & 21 & 18 & 18 & 18 & 9 \\ S-CF Tree(4) & 20 & 16 & 16 & 16 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Complexity tests (#hops) without feature selection. the second outcome, the SOM, \(k\)-Means++, and BIRCH algorithms presented almost the same patterns, with lower SSE values for each dataset. For the last outcome, all versions of the proposed S-CF Tree had the lowest SSE values for each dataset. As the BIRCH algorithm is also a tree algorithm, it was expected to have values close to those of the S-CF Tree algorithm. However, the SSE values of the BIRCH algorithm were almost twice as high. This is because the BIRCH algorithm forces its tree to be created as  
Figure 5: SOM patterns for: (**a**) the Acc. dataset; (**b**) the CSE dataset; (**c**) the Mar. dataset; (**d**) the MI dataset; (**e**) the Coo. dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Acc.** & **CSE** & **Mar.** & **MI** & **Coo.** \\ \hline DQ & 10,425 & 8579 & 6021 & 3274 & 407 \\ SOM & 6952 & 5938 & 3124 & 1952 & 185 \\ Kmeans++ & 7108 & 5459 & 3157 & 2075 & 179 \\ BIRCH & 7012 & 5798 & 3195 & 1973 & 183 \\ S-CF Tree(2) & 3341 & 2259 & 2875 & 1862 & 179 \\ S-CF Tree(3) & 3358 & 2129 & 2856 & 1903 & 176 \\ S-CF Tree(4) & 3398 & 2332 & 2882 & 1907 & 181 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy tests without feature selection. a balanced tree. Although the BIRCH algorithm is a very efficient algorithm, in terms of search pattern, it obtained patterns close to those of standard clustering algorithms in our study focused on capturing similar job postings. In Tables 4 and 5, the complexity analyses for the five different job groups are detailed, where we selected all features using the FP-Growth algorithm. The first outcome of this analysis is that due to the dimensionality reduction operations, noticeable increases in performance were observed for all datasets. As the second outcome, the best performances in terms of both time and number of hops were observed for algorithms using a tree data structure. However, in all versions of the proposed S-CF Tree algorithm, better performances were achieved in terms of time compared to the BIRCH algorithm. Finally, for all datasets, it was found that the variety of the number of clusters in any entity in the S-CF Tree did not have an effect on the hop counts and time. In Table 6, the complexity analyses performed for five different job groups are detailed, again eliminating some features with the FP-Growth algorithm. The analyses were performed by measuring the accuracy according to the SSE values. The accuracy values of DQs were obtained similar to those in Table 3. The values in Table 6 show that the SSE values slightly increased as some significant features had been eliminated by FP-Growth. It can be observed that the SSE values of the S-CF Tree versions increased, which presented the highest accuracy values by between 6% and 7%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Acc.** & **CSE** & **Mar.** & **MI** & **Coo.** \\ \hline DQ & 12,982 & 10,079 & 7129 & 3609 & 598 \\ SOM & 7235 & 7001 & 3862 & 2198 & 190 \\ Kmeans++ & 7249 & 6909 & 3871 & 2012 & 187 \\ BIRCH & 7148 & 5991 & 3810 & 2911 & 190 \\ S-CF Tree(2) & 3597 & 2388 & 3019 & 2617 & 187 \\ S-CF Tree(3) & 3590 & 2398 & 3029 & 2603 & 182 \\ S-CF Tree(4) & 3599 & 2487 & 3022 & 2612 & 182 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy tests without feature selection (FP-Growth).