# Determining the Quality of a Dataset in Clustering Terms
## 3 Results
### Results for Resellers
The full results of the NMI and Fowlkes-Mallows indices are presented in Tables 7 and 8, respectively. For divisions using graph methods with a cut-off parameter of 0.25, we obtained poor values for the modularity parameter (below 0.1). In the DBSCAN method, we only received one group. Most of the customers were not classified into it. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **L.25** & **L.5** & **GM.25** & **GM.5** & **LP.25** & **LP.5** & **DBS** & **KM** \\ \hline
**L.25** & 1 & 0.72 & 0 & 0.95 & 0 & 0.95 & 0.21 & 0.05 \\
**L.5** & 0.72 & 1 & 0.29 & 0.7 & 0 & 0.7 & 0.16 & 0.12 \\
**GM.25** & 0 & 0.29 & 1 & 0 & 0 & 0 & 0 & 0.09 \\
**GM.5** & 0.95 & 0.7 & 0 & 1 & 0 & 1 & 0.21 & 0.05 \\
**LP.25** & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\
**LP.5** & 0.95 & 0.7 & 0 & 1 & 0 & 1 & 0.21 & 0.05 \\
**DBS** & 0.21 & 0.16 & 0 & 0.21 & 0 & 0.21 & 1 & 0.05 \\
**KM** & 0.05 & 0.12 & 0.09 & 0.05 & 0 & 0.05 & 0.05 & 1 \\ \hline \end{tabular}
\end{table} 
Table 7: NMI index for wholesale customers clustering. Figure 4: Histogram of scaled cosine similarity for the dataset of resellers. The Calinski-Harabasz and Davies-Bouldin index values for the different methods and cut-off parameters are included in Table 9.  
The modularity values for different graph methods and different cut-off parameters are presented in Table 10. The table does not include parameter values for divisions with one group. This highest value of the Modularity parameter corresponds to the Greedy Modularity and Label Propagation method for a cut-off parameter of 0.5. The dataset tested was not a good candidate for clustering, as indicated by the results obtained. Despite the use of advanced data processing methods and the use of various clustering algorithms, the data contained in this set were too homogeneous (or too diverse) for the clustering to be able to reasonably propose a division into groups. It was indicated by the modularity of the parameter being below the suggested value of 0.3. The summary of all obtained groups and their sizes in each method is presented in Table 11. Please take note that group numbers in one division do not correspond to group numbers in other divisions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cut-Off Parameter** & **Method** & **Modularity** \\ \hline
0.5 & Louvain & 0.279 \\
**0.5** & **Greedy** & **0.290** \\
**0.5** & **Label** & **0.290** \\
0.25 & Louvain & 0.086 \\
0.25 & Greedy & 0.053 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Modularity values for different graph methods and different cut-off parameters (wholesale customers). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **L.25** & **L.5** & **GM.25** & **GM.5** & **LP.25** & **LP.5** & **DBS** & **KM** \\ \hline
**L.25** & 1 & 0.84 & 0.5 & 0.99 & 0.71 & 0.99 & 0.64 & 0.56 \\
**L.5** & 0.84 & 1 & 0.55 & 0.84 & 0.61 & 0.84 & 0.59 & 0.47 \\
**GM.25** & 0.50 & 0.55 & 1 & 0.5 & 0.71 & 0.5 & 0.6 & 0.57 \\
**GM.5** & 0.99 & 0.84 & 0.5 & 1 & 0.71 & 1 & 0.64 & 0.56 \\
**LP.25** & 0.71 & 0.61 & 0.71 & 0.71 & 1 & 0.71 & 0.85 & 0.75 \\
**LP.5** & 0.99 & 0.84 & 0.5 & 1 & 0.71 & 1 & 0.64 & 0.56 \\
**DBS** & 0.64 & 0.59 & 0.6 & 0.64 & 0.85 & 0.64 & 1 & 0.62 \\
**KM** & 0.56 & 0.47 & 0.57 & 0.56 & 0.75 & 0.56 & 0.62 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: FMI index for wholesale customers clustering. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cut-Off Parameter** & **Method** & **CH Index** & **DB Index** \\ \hline
0.5 & Louvain & 44.240 & 3.785 \\
**0.5** & **Greedy** & **69.146** & **3.163** \\
**0.5** & **Label** & **69.146** & **3.163** \\ - & DBSCAN & 59.238 & 2.869 \\ - & \(k\)-means & 59.579 & 3.167 \\
0.25 & Louvain & 68.947 & 3.167 \\
0.25 & Greedy & 22.983 & 5.445 \\
0.25 & Label & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Calinski–Harabasz (CH) and Davies–Bouldin (DB) index values for the different methods and different cut-off parameters (wholesale customers).