# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 5 Discussion and Conclusions
Thus, the results obtained with the K-Mean method better reflect values that differ significantly from the average (they better reflect the \"fluctuating\" values), while the K-Medoid method compensates the fluctuations. This characteristic manifested itself in both cases, both for companies in Hungary and for companies in Romania. 5. The focus of the research was not on the difference between the two \"countries\" but on the analysis of two \"different databases\". As we have shown, the study identified significant differences both in the number of elements of the two databases and in the homogeneity of the databases. Therefore, there is a need for grouping the similar elements of a database. For smaller, more homogeneous, and larger, heterogeneous databases, the two grouping methods (K-Mean and K-Medoid) lead to different results. In terms of procedure, the two databases could be merged, but the authors aimed to demonstrate that even for different data (smaller, larger, more homogeneous, or less homogeneous), there is a significant difference between the results of the two procedures. All the hypotheses presented at the beginning were successfully supported by empirical data. It is indisputable that the two different grouping methods applied to the same database give different results. For financial ratios, these methods led to different performance evaluations of food retail companies. In the case of a large number of elements in databases from a statistical point of view, taking into account that the K-Medoid method is less sensitive to extreme and fluctuating values, this method has created balanced numbers of the units within the groups (each group has more than 10% of the total database). For K-Mean, it was not uncommon to have \"only a few units groups\", which means 1%, 2% or 3% of the total database (this also supports the sensitivity of the K-Mean method for outliers). Even though outliers were eliminated from the database, there are still several swinging values in the database which are not statistically representative values. The essence of cluster analysis is to find the common features of the sample population. From this perspective, the authors recommend the K-Medoid method for statistical analysis of large databases, while the K-Mean clustering method is recommended for the evaluation of more detailed analyses that also focus on fluctuating values. Moreover, our findings regarding the financial performance of food retail businesses prove that their performance is unfavorable and even very weak. Especially in the case of Romanian companies, there are a lot of enterprises that achieve negative results, which produce losses not only in a given year, but year after year. In such circumstances, the question arises of how these businesses will manage to survive in the future and possibly develop even in the current difficult circumstances. The expansion of hypermarkets and supermarkets, globalization and the online market have pushed \"traditional\" food retailers into a difficult situation characterized by low long-term investments in these businesses, accumulated losses from previous years and very low levels of profit margins applied. Taking all these into account, the level of financial performance of \"traditional\" food retailers is very low. It is worth mentioning that in this industry there is a significant number of companies (approx. 28% of total active companies in Romania) and a large number of employees (almost 1 million employees) [67]. Such challenges need to be managed by \"traditional\" retail businesses in order to survive and even to gain competitive advantages over their main competitors. In contrast to large shopping centers, the \"traditional\" shops are much more customer-centered, have a more familiar atmosphere and geographical location benefits, offering local products to consumers, and provide more direct relationships [52]. As regards pricing policy, they cannot compete with modern chain stores, but they can offer higher quality and local products.