# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 4 Research Results
### Cluster Analysis Results: An Overall Picture  
The Figure 4a,b show the results of the Elbow method used to determine the cluster numbers (plotting of cluster numbers can show the break point--Elbow criterion, which represents the optimal cluster number). As a result, five clusters were used for Hungarian companies and fifteen for Romanian companies.  
Furthermore, the dendrograms (see Figure 5a,b), obtained using hierarchical cluster analysis (Ward's method and squared Euclidian distance), have confirmed the number of clusters, five in the case of Hungarian companies, and fifteen in the case of Romanian companies.  
In the next step, the structure of the clusters was based on both the K-Mean and the K-Medoid method. We have worked with the same number of clusters in the case of the K-Mean method as well as the K-Medoid method mainly due to the aim of this study, i.e., to perform a comparative analysis of the financial performance of companies based on the results of the two methods, focusing on the difference between them.  
We can see quite a big difference in the number of companies included in the K-Mean and K-Medoid groups, as shown in Figure 6a,b. As can be seen, the K-Mean method has a much more varied number of companies within the group, and the K-Medoid has a more even distribution of companies, both for Hungarian (the database with fewer units) and Romanian companies (the bigger database).  
Figure 4: Number of clusters based on Elbow method: (**a**) Hungarian companies. (**b**) Romanian companies. Source: Authors’ own editing, based on own calculations.  
Figure 3: Link between financial performance indicators: (**a**) Hungarian companies. (**b**) Romanian companies. Source: Authors’ own editing, based on own calculations.  
The results obtained using the K-Mean method for the Hungarian companies highlighted that the number of companies included in cluster no. 5 (3 companies) is only 1.58% of the total companies (190 companies), while the number of companies included in cluster  
Figure 5: (**a**) Dendrogram using Ward Linkage method for Hungarian database. (**b**) Dendrogram using Ward Linkage method for Romanian database. Source: Authors’ own editing, based on own calculations.  
Figure 6: (**a**) Number of companies for Hungarian database within the clusters. (**b**) Number of companies for Romanian database within the clusters. Source: Authors’ own editing, based on own calculations.  
no. 2 (131 companies), which contains more enterprises, is 68.95% of the total companies (190 companies). In contrast, the numbers of companies in the groups obtained by the group breakdown using the K-Medoid method is much more balanced: the group with the fewest companies included contained 12.11% (cluster no. 5 with 23 companies) of total companies (190 companies) and the group with the most companies included contained 29.47% (cluster no. 2 with 56 companies) of the total companies (190).  
The results are similar for the Romanian companies, too: the K-Mean method has a much more varied number of companies within the group, compared to the K-Medoid method which has a more even distribution of companies. The number of Romanian companies included in the database was much higher (640 companies) and the differences between them were also larger, so we worked with 15 clusters. Grouping the companies with the K-Mean method, we obtained 5 clusters (clusters no. 3, 9, 11, 13, 15) with less than 1% of the companies included in these groups (which means less than 7 companies) from the total companies (640), and two clusters (clusters no. 2 and 4) with 32% (205 companies) and 33% (214 companies) of the companies included from the total.