# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 4 Research Results
### Comparative Analysis of Financial Performance Results: K-Mean vs. K-Medoid  
The average values of ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) for the five groups of the Hungarian database (190 companies) are shown in Table 6.  
Also, the differences between the results obtained from the K-Mean and K-Medoid clustering methods are clearly illustrated in Figure 7. Thus, the results obtained based on the K-Mean method better reflect the \"fluctuating\" values: for each of the three ratios, the highest and lowest values are higher than the values obtained based on the K-Medoid method (ROE: 85.26% vs. 30.95% and \(-145.06\%\) vs. \(-53.73\%\)). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clusters**} & \multicolumn{4}{c}{**K-Mean**} & \multirow{2}{*}{**Clusters**} & \multicolumn{4}{c}{**K-Medoid**} \\ \cline{2-3} \cline{5-10}  & **N \({}^{1}\)** & & **ROS** & **ROA** & **ROE** & & **N \({}^{1}\)** & **ROS** & **ROA** & **ROE** \\ \hline
1 & 9 & 6.20 & 19.37 & 85.26 & 1 & 40 & 5.48 & 16.04 & 30.95 \\
2 & 131 & 2.35 & 5.38 & 9.44 & 2 & 37 & 3.56 & 5.64 & 16.50 \\
3 & 35 & \(-9.61\) & \(-17.16\) & \(-30.22\) & 3 & 56 & 0.56 & 1.39 & 2.89 \\
4 & 12 & \(-2.39\) & \(-5.79\) & \(-120.94\) & 4 & 34 & \(-2.71\) & \(-5.15\) & \(-50.43\) \\
5 & 3 & \(-11.51\) & \(-69.47\) & \(-145.06\) & 5 & 23 & \(-14.18\) & \(-32.72\) & \(-53.73\) \\ \hline \hline \end{tabular} Note: \({}^{1}\) Number of companies in cluster. Source: Based on own calculations.  
\end{table} 
Table 6: Average financial indicator values per cluster (%) for Hungarian companies. Figure 7: Average financial indicator values per cluster for Hungarian companies: (**a**) K-Mean method; (**b**) K-Medoid method. Source: Authors’ own editing based on own calculations. Evaluating the financial results, it can be seen that there are two (K-Mean) and three (K-Medoid) groups with ROS, ROA and ROE financial ratios in the positive interval; this means that 73% of the companies grouped based on the K-Mean method and 70% of the companies grouped by the K-Medoid method have positive financial ratios. The fact that the indicators (about 70% of companies) have a positive value is the main requirement for these indicators and, obviously, the higher they are, the better the financial performance is. If compared with the existing values in the specialized literature, these positive values fall within the \"normal/existing\" values, but the problem appears in the case of clusters (companies) that register negative values. The most visible differences are in case of the ROE indicator for all five clusters. The number of Romanian companies included in the database and the differences between them were also larger, so we worked with fifteen clusters. The results are shown in Table 7 and Figure 8.  
The characteristics of the Hungarian database are also present here: the results obtained with the K-Mean method better reflect values that differ significantly from the average, while the K-Medoid method compensates the fluctuations. The range of values