# Data Analysis for Information Discovery
## 8 Experiments and Results
### Gender Gap in Spanish WP Dataset
#### 8.1.2 Clustering and Silhouettes Using Normalized Data  
The original dataset was normalized between 0 and 1. Applying PCA to this dataset, losing less than 2% of the total variation in the data, produced a dataset with six dimensions.  
The following Tables 4-6 show the obtained results in terms of silhouettes varying the number of clusters from 10 to 50 (see columns). Each row represents the percentage of points with a silhouette greater than a given threshold, that is 0.7, 0.8, and 0.9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 0 & 1.92 & 1.96 & 0.02 & 0.17 \\ \hline
**0.8** & 0 & 0.04 & 0 & 0.02 & 0.17 \\ \hline
**0.9** & 0 & 0.04 & 0 & 0.02 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Clustering performance in terms of silhouettes using the reduced dataset with SPQR applied to normalized data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 43.95 & 37.44 & 43.28 & 50.46 & 60.81 \\ \hline
**0.8** & 21.60 & 16.29 & 29.31 & 39.40 & 51.45 \\ \hline
**0.9** & 0 & 0.40 & 16.75 & 35.92 & 37.340 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clustering performance in terms of silhouettes using the reduced dataset with PCA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 43.95 & 37.44 & 43.28 & 50.46 & 60.81 \\ \hline
**0.8** & 21.60 & 16.29 & 29.31 & 39.40 & 51.45 \\ \hline
**0.9** & 0 & 0.40 & 16.75 & 35.92 & 37.340 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clustering performance in terms of silhouettes using original data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 92.54 & 76.06 & 78.93 & 82.49 & 87.80 \\ \hline
**0.8** & 89.55 & 69.64 & 69.59 & 75.41 & 74.40 \\ \hline
**0.9** & 78.59 & 47.83 & 57.753 & 46.103 & 48.463 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clustering performance in terms of silhouettes using the reduced dataset with SPQR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 43.95 & 37.44 & 43.28 & 50.46 & 60.81 \\ \hline
**0.8** & 21.60 & 16.29 & 29.31 & 39.40 & 51.45 \\ \hline
**0.9** & 0 & 0.40 & 16.75 & 35.92 & 37.340 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Clustering performance in terms of silhouettes using the reduced dataset with PCA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 8.81 & 3.37 & 2.61 & 2.40 & 1.85 \\ \hline
**0.8** & 0 & 1.60 & 0 & 2.21 & 1.01 \\ \hline
**0.9** & 0 & 0 & 0 & 0.46 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Clustering performance in terms of silhouettes using normalized data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Silhouette\textbackslash{}N. Clusters** & **10** & **20** & **30** & **40** & **50** \\ \hline
**0.7** & 0.06 & 0.08 & 0.17 & 0.08 & 0.23 \\ \hline
**0.8** & 0.06 & 0.08 & 0.10 & 0.08 & 0.15 \\ \hline
**0.9** & 0.06 & 0.08 & 0.06 & 0.06 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Clustering performance in terms of silhouettes using the reduced dataset with PCA applied to normalized data.  
In these experiments, the results obtained using normalized data seem to be worse than those obtained with the raw data.