## 2 Materials and Methods
### Data Preparation
Following data reduction, six datasets (All datasets are available upon request) were created. The initial dataset was labeled Dataset 1 and consisted of 3914 entries and 11 features. The first 10 features were the geochemical data of the major elements and the last was the class. A snapshot of Dataset 1, the initial dataset, is shown in Table 1. These 3914 data samples were used to plot SiO\({}_{2}\) vs. K\({}_{2}\)O [42] (Figure 5) and SiO\({}_{2}\) vs. Na\({}_{2}\)O + K\({}_{2}\)O [43] (Figure 6) to check if the four volcanic regions could be discriminated by the use of this bigger dataset. The diagrams show that this was not the case, as major overlapping of the sample points was present. In Dataset 2, the zeroes were replaced by the mean value. In Dataset 3, the zeroes were replaced by the median. Datasets 4-6 were created from Datasets 1-3 correspondingly by deleting the outliers. The outliers were removed by using the stats class of the Scipy library and the Numpy library. The number of entries of each class in every dataset is shown in Table 2. The basic statistic data of Datasets 1-6 are presented in Tables 3-8. The histograms of Datasets 1-3 with outliers are depicted in Figure 7. The histograms of Datasets 4-6 with the outliers removed are depicted in Figure 8. The histogram of Dataset 5 shows the best distribution of the data. Figure 5: K\({}_{2}\)O vs. SiO\({}_{2}\) diagram. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Feat.\#1** & **Feat.\#2** & **Feat.\#3** & **Feat.\#4** & **Feat.\#5** & **Feat.\#6** & **Feat.\#7** & **Feat.\#8** & **Feat.\#9** & **Feat.\#10** \\ \hline
**SiO\({}_{2}\)** & **TiO2** & **Al2O\({}_{5}\)** & **Fe2O\({}_{3}\)T** & **CaO** & **MgO** & **MnO** & **K\({}_{2}\)O** & **Na\({}_{2}\)O** & **P\({}_{2}\)O\({}_{5}\)** \\ \hline
67.72 & 0.32 & 15.7 & 2.75 & 3.63 & 1.23 & 0.07 & 3.36 & 3.16 & 0.11 & 1 \\
67.5 & 0.36 & 15.15 & 3.30 & 4.85 & 1.69 & 0.08 & 3.69 & 2.55 & 0.17 & 1 \\
60.55 & 0.52 & 17.26 & 5.61 & 6.26 & 2.49 & 0.11 & 2.25 & 2.98 & 0.14 & 1 \\
71.58 & 0.25 & 12.72 & 2.07 & 1.49 & 0.5 & 0.06 & 4.19 & 3.01 & 0.03 & 2 \\
69.13 & 0.38 & 14.06 & 2.79 & 2.23 & 0.67 & 0.09 & 3.93 & 3.47 & 0.07 & 2 \\
71.41 & 0.38 & 14.05 & 2.56 & 2.73 & 0.77 & 0.04 & 3.32 & 4.02 & 0.08 & 2 \\
67.89 & 0.39 & 14.11 & 2.84 & 2.43 & 0.64 & 0.11 & 3.70 & 3.73 & 0.08 & 2 \\
54.28 & 0.84 & 16.97 & 8.11 & 8.52 & 5.56 & 0.15 & 1.57 & 3.07 & 0.12 & 3 \\
52.21 & 0.84 & 17.79 & 8.70 & 10.81 & 6.17 & 0.16 & 0.60 & 2.71 & 0.10 & 3 \\
52.3 & 0.85 & 17.67 & 8.52 & 10.73 & 6.13 & 0.16 & 0.60 & 2.78 & 0.11 & 3 \\
55.31 & 0.92 & 19.19 & 7.18 & 8.55 & 4.11 & 0.12 & 1.09 & 3.61 & 0.20 & 4 \\
55.52 & 0.92 & 18.99 & 7.15 & 8.51 & 4.30 & 0.12 & 1.09 & 3.42 & 0.22 & 4 \\
54.91 & 0.92 & 19.51 & 7.17 & 8.61 & 4.34 & 0.12 & 1.08 & 3.61 & 0.19 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Snapshot of Dataset 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Methane** & **Melos** & **Santorini** & **Nysiros** & **Total** \\  & **(Class 1)** & **(Class 2)** & **(Class 3)** & **(Class 4)** & **Total** \\ \hline Dataset 1 & 274 & 115 & 1022 & 2503 & 3914 \\ Dataset 2 & 274 & 115 & 1022 & 2503 & 3914 \\ Dataset 3 & 274 & 115 & 1022 & 2503 & 3914 \\ Dataset 4 & 135 & 104 & 908 & 2275 & 3422 \\ Dataset 5  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **Feat.\#1** & **Feat.\#2** & **Feat.\#3** & **Feat.\#4** & **Feat.\#5** & **Feat.\#6** & **Feat.\#7** & **Feat.\#8** & **Feat.\#9** & **Feat.\#10** \\ \hline  & **SiO\({}_{\mathbf{2}}\)** & **TiO\({}_{\mathbf{2}}\)** & **Al2O\({}_{\mathbf{3}}\)** & **Fe2O\({}_{\mathbf{3}}\)T** & **CaO** & **MgO** & **MnO** & **K2O** & **Na2O** & **P2O\({}_{\mathbf{5}}\)** \\ \hline mean & 55.44 & 1.23 & 18.00 & 6.97 & 7.95 & 7.98 & 0.19 & 1.28 & 3.67 & 0.20  
Figure 7: Histograms of the datasets with outliers: (**a**) Dataset 1, (**b**) Dataset 2, and (**c**) Dataset 3.