# Determining the Quality of a Dataset in Clustering Terms
## 3 Results
### Results for Retail Customers
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **L.25** & **L.5** & **L.75** & **GM.25** & **GM.5** & **GM.75** & **LP.25** & **LP.5** & **LP.75** & **DBS** & **KM** \\ \hline
**L.25** & 1 & 0.91 & 0.6 & 0.84 & 0.83 & 0.79 & 0 & 0 & 0.81 & 0.62 & 0.67 \\
**L.5** & 0.91 & 1 & 0.61 & 0.79 & 0.84 & 0.8 & 0 & 0 & 0.82 & 0.63 & 0.64 \\
**L.75** & 0.6 & 0.61 & 1 & 0.55 & 0.6 & 0.68 & 0 & 0 & 0.67 & 0.55 & 0.48 \\
**GM.25** & 0.84 & 0.79 & 0.55 & 1 & 0.76 & 0.7 & 0 & 0 & 0.71 & 0.58 & 0.73 \\
**GM.5** & 0.83 & 0.84 & 0.6 & 0.76 & 1 & 0.8 & 0 & 0 & 0.83 & 0.62 & 0.63 \\
**GM.75** & 0.79 & 0.8 & 0.68 & 0.7 & 0.8 & 1 & 0 & 0 & 0.93 & 0.64 & 0.59 \\
**LP.25** & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 \\
**LP.5** & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 \\
**LP.75** & 0.81 & 0.82 & 0.67 & 0.71 & 0.83 & 0.93 & 0 & 0 & 1 & 0.63 & 0.6 \\
**DBS** & 0.62 & 0.63 & 0.55 & 0.58 & 0.62 & 0.64 & 0 & 0 & 0.63 & 1 & 0.52 \\
**KM** & 0.67 & 0.64 & 0.48 & 0.73 & 0.63 & 0.59 & 0 & 0 & 0.60 & 0.52 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NMI index for retail customers clustering. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **L.25** & **L.5** & **L.75** & **GM.25** & **GM.5** & **GM.75** & **LP.25** & **LP.5** & **LP.75** & **DBS** & **KM** \\ \hline
**L.25** & 1 & 0.98 & 0.76 & 0.95 & 0.95 & 0.92 & 0.71 & 0.71 & 0.94 & 0.8 & 0.87 \\
**L.5** & 0.98 & 1 & 0.76 & 0.93 & 0.96 & 0.93 & 0.7 & 0.7 & 0.94 & 0.8 & 0.85 \\
**L.75** & 0.76 & 0.76 & 1 & 0.73 & 0.76 & 0.79 & 0.59 & 0.59 & 0.79 & 0.7 & 0.7 \\
**GM.25** & 0.95 & 0.93 & 0.73 & 1 & 0.92 & 0.87 & 0.71 & 0.71 & 0.88 & 0.78 & 0.9 \\
**GM.5** & 0.95 & 0.96 & 0.76 & 0.92 & 1 & 0.93 & 0.71 & 0.71 & 0.94 & 0.8 & 0.85 \\
**GM.75** & 0.92 & 0.93 & 0.79 & 0.87 & 0.93 & 1 & 0.71 & 0.71 & 0.98 & 0.8 & 0.8 \\
**LP.25** & 0.71 & 0.7 & 0.59 & 0.71 & 0.71 & 0.71 & 1 & 1 & 0.71 & 0.6 & 0.71 \\
**LP.5** & 0.71 & 0.7 & 0.59 & 0.71 & 0.71 & 0.71 & 1 & 1 & 0.71 & 0.6 & 0.71 \\
**LP.75** & 0.94 & 0.94 & 0.79 & 0.88 & 0.94 & 0.98 & 0.71 & 0.71 & 1 & 0.8 & 0.82 \\
**DBS** & 0.8 & 0.8 & 0.7 & 0.78 & 0.8 & 0.8 & 0.6 & 0.6 & 0.8 & 1 & 0.75 \\
**KM** & 0.86 & 0.85 & 0.7 & 0.9 & 0.85 & 0.8 & 0.71 & 0.71 & 0.82 & 0.75 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: FMI index for retail customers clustering. Table 5 presents the Modularity parameter values for different graph methods and different cut-off parameters. The highest value corresponded to the Louvain method with a cut-off parameter of 0.75. The considered dataset was subject to clustering, in most cases into three groups. Unsurprisingly, the greatest modularity came with a cut-off parameter of 0.75. This level indicated a high similarity between vertices, so edges existed between the most similar customers. As the cut-off parameter decreased, more and more edges appeared in the graph, which could have a negative effect on the quality of the clustering. The summary of all obtained groups and their sizes in each method is presented in Table 6. Please take note that group numbers in one division do not correspond to group numbers in other divisions.