# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 5 Discussion and Conclusions  
The fundamental research hypothesis of this paper was as follows: in the case of large databases with variable quantitative data, cluster analysis is necessary in order to obtain accurate results and the clustering method chosen can yield different results. Therefore, this study has pointed out that the results obtained using the two grouping methods (K-Mean and K-Medoid) are different. These findings are in line with other research papers [23; 24; 31; 33] that have highlighted the advantages and disadvantages of the two methods. Moreover, our analysis identified significant differences between the results of these non-hierarchical clustering methods in terms of intra-group numbers: they are more uniform in case of the K-Medoid method than in case of the K-Mean method. Furthermore, this study emphasizes that the K-Medoid method is less sensitive to extremes and that it has better distribution within the group, with more balanced values. In the case of the K-Mean method, the grouping procedure is more sensitive and the range between the values of the units in the group is larger; at the same time, it gives a more detailed picture of the phenomenon under investigation. Based on these results and the hypotheses assumed at the beginning of the research, we can state the following: \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Financial Performance of**} & \multicolumn{2}{c}{**Financial Performance of**} \\ \cline{2-5}
**ROS, ROA, ROE** & \multicolumn{2}{c}{**Hungarian Companies**} & \multicolumn{2}{c}{**Romanian Companies**} \\ \cline{2-5}  & **K-Mean** & **K-Medoid** & **K-Mean** & **K-Medoid** \\ \hline Good & 4.74\% & 40.53\% & 33.44\% & 19.69\% \\ Acceptable/Weak & 68.95\% & 29.47\% & 1.56\% & 17.81\% \\ Negative & 26.31\% & 30.00\% & 65.00\% & 62.50\% \\ Total & 100.00\% & 100.00\% & 100.00\% & 100.00\% \\ \hline \hline \end{tabular} Source: Based on own calculations.  
\end{table} 
Table 8: Financial performance of Hungarian and Romanian food retail companies. Processing the financial statements of the companies included in the database, we analyzed the most common statistical indicators (average, dispersion, deviation, standard deviation, kurtosis, skewness) for the financial performance indicators (ROS, ROA and ROE). The results have shown clearly that the companies are heterogeneous with a very large range of data for all indicators. An analysis of the standard deviation values was used to show the variability of the data and the differences between the individual data. These results confirmed our first hypothesis that the database contains highly variable elements. 2. The results of the descriptive statistical indicators confirmed our assumptions that we cannot examine the financial performance of the companies in the database based only on the average values of the financial indicators. Therefore, it is necessary to group the sample population into several homogeneous groups based on certain key indicators. The average values cannot be representative. 3. In order to achieve homogenous clusters, the key indicators were selected in accordance with the objectives of the research. Therefore, we selected the three main indicators having the most comprehensive characteristics of financial performance, which are the most accepted in the literature, too (ROS, ROA and ROE). The selection and determination of the indicators or characteristics based on which the groups will be formed are very important, as they can influence the obtained results. These indicators or characteristics must reflect the aim of the research. Of course, selecting other indicators can lead to a different group composition. 4. The results of the two applied grouping methods (K-Mean and K-Medoid) supported our main assumption that the chosen and applied method leads to different results.