## 3 Results and Discussions
### Results of Predicting Age Information for Unlabeled Datasets and Exploration
#### 3.4.3. On the Quantification of Age Difference with Fuzzy Sets  
Figure 13 illustrates a correlation-similarity matrix, which mapped each age group pair in a subsection using both similarity and correlation values. Moreover, using the correlation-similarity matrix, the uncertain differences between age groups could be roughly classified into three areas. For instance, Figure 13 shows that (10s, 20s) belonged to area 1, implying that they were relatively close to each other. Conversely, (20s, \(\geq\)50s) were classified as area 3, showing a relatively large age difference. Lastly, (10s, 30s) were classified into area 2, which indicates they were neither relatively high nor low. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**Age Group Pairs** & \multicolumn{2}{c}{**Rank**} & & & & \\ \cline{2-6}
**(g\({}_{i}\), \(g\))** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{(20s, 30s)} & National Assembly/Political Party & The Press & President\({}^{\prime}\)s Office & Politics General & Global Economy \\ \cline{2-6}
**(20s, 40s)** & North Korea & National Defense/Diplomacy & National Assembly/Political Party & USA/Latin America & President\({}^{\prime}\)s Office \\ \hline \multirow{2}{*}{(20s, \(\geq\)50s)} & Car/Test Drive & Human Rights/Welfare & Real Estate & Food/Restaurant & Living Economy \\ \hline \multirow{2}{*}{(30s, 40s)} & National Defense/Diplomacy & North Korea & Middle East/Africa & President\({}^{\prime}\)s Office & Religion \\ \hline \multirow{2}{*}{(30s, \(\geq\)50s)} & Education & Fashion/Beauty & Mobile & Games/Reviews & Performance/Exhibition \\ \hline \multirow{2}{*}{(40s, \(\geq\)50s)} & Games/Reviews & Fashion/Beauty & Weather & Human Rights/Welfare & Europe \\ \hline \multicolumn{6}{c}{**(b) Bottom 5 Subsections**} & & & & \\ \hline
**Age Group Pairs** & \multicolumn{2}{c}{**Rank**} & & & \\ \cline{2-6}
**(g\({}_{i}\), \(g\))** & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{2}{*}{(10s, 20s)} & Environment & Security/Hacking & National Defense/Diplomacy & Education & Weather \\ \hline \multirow{2}{*}{(10s, 30s)} & Fashion/Beauty & Games/Reviews & Human Rights/Welfare & Internet/SNS & Education \\ \hline \multirow{2}{*}{(10s, 40s)} & Real Estate & Road/Traffic & Mobile & Internet/SNS & The Press \\ \hline \multirow{2}{*}{(10s, \(\geq\)50s)} & National Defense/Diplomacy & North Korea & World General & The Press & President\({}^{\prime}\)s Office \\ \hline \multirow{2}{*}{(20s, 30s)} & Human Rights/Welfare & Fashion/Beauty & Games/Reviews & Food/Medical & Internet/SNS \\ \hline \multirow{2}{*}{(20s, 40s)} & Fashion/Beauty & Mobile & Games/Reviews & Internet/SNS & Car/Test Drive \\ \hline \multirow{2}{*}{(20s, \(\geq\)50s)} & North Korea & National Defense/Diplomacy & USA/Latin America & President\({}^{\prime}\)s Office & National Assembly/Political Party \\ \hline \multirow{2}{*}{(30s, 40s)} & Mobile & Communications/ New Media & Car/Test Drive & IT General & Road/Traffic \\ \hline \multirow{2}{*}{(30s, \(\geq\)50s)} & President\({}^{\prime}\)s Office & National Assembly/Political Party & Politics General & North Korea & Real Estate \\ \hline \multirow{2}{*}{(40s, \(\geq\)50s)} & Politics General & National Assembly/Political Party & National Defense/Diplomacy & North Korea & USA/Latin America \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._Using the results of the correlation-similarity matrix, this study compiled Table 12, which shows the membership of an age group in three fuzzy sets of age differences. Using Table 12, the difference between the age groups could be measured by considering the uncertainty in their relationships, and the following results were obtained. In the case of (10s, 20s), the difference between them was not considered large, as they belonged to the fuzzy set \(D_{\text{Low}}\) with a 100% degree of membership, i.e., \(\mu_{D_{\text{Low}}}(10s,20s)=1.0\).