## 6 Experimental Results
### Knowledge Exploration and Visualization
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{1}{|c|}{\multirow{2}{*}{**Dataset**}} & \multicolumn{6}{c|}{**Cluster ID**} \\ \cline{2-10}  & **Weight** & **Cluster1** & **Cluster2** & **Cluster3** & **Cluster4** & **Cluster5** & **Cluster6** & **Cluster7** & **Cluster8** & **Cluster9** \\ \hline \multirow{5}{*}{**USA**} & TF-IDF & 215 & 176 & 159 & 139 & 99 & 93 & 49 & 25 & 19 & 15 \\ \cline{2-10}  & TF-Entropy & 228 & 167 & 166 & 135 & 106 & 75 & 54 & 27 & 16 & 15 \\ \cline{2-10}  & LegTF-Interp & 225 & 222 & 191 & 183 & 178 & & & & & \\ \cline{2-10}  & LegTF-Entropy & 223 & 191 & 184 & 183 & 105 & 103 & & & & \\ \cline{2-10}  & Bot-IDF & 236 & 223 & 191 & 181 & 158 & & & & \\ \cline{2-10}  & Bot-History & 230 & 223 & 192 & 177 & 167 & & & & \\ \hline \multirow{5}{*}{**LDA**} & TF-IDF & 205 & 193 & 187 & 180 & 144 & 21 & 19 & 14 & 13 & 13 \\ \cline{2-10}  & TF-Entropy & 464 & 406 & 91 & 8 & 7 & 5 & 5 & 3 & & \\ \cline{2-10}  & LegTF-IDF & 428 & 236 & 197 & 113 & 15 & & & & & \\ \cline{2-10}  & LegTF-Entropy & 827 & 160 & 1 & 1 & 0 & & & & \\ \cline{2-10}  & Bot-IDF & 230 & 215 & 194 & 188 & 162 & & & & \\ \hline \end{tabular}
\end{table} 
Table 10: Experimental results for D6. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|c|c|} \hline \multicolumn{1}{|c|}{\multirow{2}{*}{**Dataset**}} & \multicolumn{6}{c|}{**LDA**} \\ \cline{2-10}  & **Weight** & **K-ASA** & **K-clus** & **GSI** & **ASI** & **Wie-Sil** \\ \hline \multirow{5}{*}{**Dataset**} & TF-IDF & 56 & 10 & 0.098 & 0.087 & 0.136 & \multirow{5}{*}{**Dataset**} & TF-IDF & 14 & 7.662 & 0.085 & 1.902 \\ \cline{2-10}  & TF-Entropy & 59 & 9 & 0.106 & 0.092 & 0.142 & \multirow{5}{*}{**D5**} & TF-Entropy & 4 & 8.556 & 0.081 & 1.782 \\ \cline{2-10}  & LGPTF-DEF & 33 & 5 & 0.100 & 0.092 & 0.144 & \multirow{5}{*}{**D5**} & LogTF-Entropy & 14 & 7.776 & 0.094 & 1.754 \\ \cline{2-10}  & LogTF-Entropy & 35 & 5 & 0.098 & 0.090 & 0.140 & \multirow{5}{*}{**D6**} & LogTF-Entropy & 4 & 8.622 & 0.080 & 1.743 \\ \cline{2-10}  & Bot-IDF & 24 & 8 & 0.127 & 0.112 & 0.163 & & & & \\ \cline{2-10}  & Bot-History & 26 & 15 & 0.120 & 0.117 & 0.167 & & & & \\ \hline \end{tabular}
\end{table} 
Table 9: Experimental results for D5. colors were defined, based on the correlation range: black represents the highest range, 0.87-1.00; dark gray represents the range 0.75-0.87; gray is used for the range 0.62-0.75; light gray is associated with the range 0.5-0.62; and white represents the lowest range, 0.0-0.5. Documents are sorted according to their category, and then the dot products between all document pairs are calculated. Figure 3 (left) shows how the different weighting functions TF-IDF and LogTF-Entropy impact the document collection. In both functions, the five macro-categories are depicted as five dark squares of similar size, showing the highest similarity between documents. So, considering two documents belonging to the same macro-category, they will tend to be more similar to each other than those belonging to other macro-categories; LogTF-Entropy (Figure 3) (bottom-left) allows modeling the five macro-categories better than TF-IDF (Figure 3) (top-left), and also characterizes some topics, whereas TF-IDF shows possible correlations between the different categories. Figure 3 (on the right) shows the correlation matrix maps for the best partitions identified by ESCAPE; LogTF-Entropy (Figure 3 bottom-right) correctly finds the dataset categories, whereas TF-IDF (Figure 3 top-right) also highlights some relevant subtopics in the same category. The importance of words within documents is determined by the weights; therefore, it is important to assess how the model is affected by different weighting schemes. For the representative dataset D1, ESCAPE computes the histogram of the TF-IDF and LogTF-Entropy weights. The LogTF-Entropy values are almost uniformly distributed in the range [0,1] (Kurtosis index \(>\) 0 and standard deviation 0.5).