# Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation
## 2 Brief Literature Review  
There is a great deal of literature dealing with the processing of databases containing large and varied elements and the breakdown of data into homogeneous groups. Clustering methods are used in many fields such as biology, disease classification, archeology, image segmentation, social classifications and market segmentation, as well as database analysis in many fields [8], e.g., entrepreneurship [9], transportation [10], medical research [11], engineering [12] and industrial clusters performance [13]. In the field of economics and finance, large and complex data and databases are needed more and more. As a result, there is a growing need for the processing of economic and financial data. From the vast amount of information available to decision makers, only the information most valuable and useful to them must be selected. In the case of economic-financial analyses, the literature describes a large number of grouping methodsused in various fields: risk analysis [14], financial risk analysis [15; 16], selection of financial ratios [17], economic fraud activities [18], real estate portfolio analysis [19], financial performance analysis [20]. [PERSON] et al. [21] provided great insight into clustering methods, underling their advantages and disadvantages for financial datasets. They demonstrated that density-based clustering does not suit financial datasets, whereas K-Mean gives the best number of clusters to help understand financial data classification. Group disaggregation methods are also very suitable for grouping companies, as demonstrated by [PERSON] et al. [22]. These authors applied clustering methods to 106 enterprises based on economic indicators (Economic Value Added, Net Income, Current Sales, Equity and Stock Price). [PERSON] et al. [23] defined clustering \"as a process of grouping data objects into disjoint clusters so that the data in each cluster are similar, yet different to the other clusters\" (p. 42). There are several methods and techniques by which these clusters are created, such as K-Mean and K-Medoid. According to [PERSON] et al. [23], the K-Mean method is a simple and easy-to-use method. Each cluster is composed based on average values, and the values/elements attached to a cluster are the closest to this average. However, the same authors [23] also pointed out two major disadvantages of the method: sensitivity to extreme values and the lack of knowledge of the number of clusters. [PERSON] and [PERSON] [24] described K-Means as a method that groups the values from a database into certain groups so that the level of similarity of the data within a group is as high as possible, whereas when compared to the other groups it is as small as possible. The level of similarity is determined based on the distance between the values of each element in the group and the group mean. The authors emphasized the simplicity of the method [24]. The K-Mean clustering method was analyzed by [PERSON] et al. [25] in terms of advantages, improvements of the classic method, strengths and weaknesses of the existing implementation of hybrid K-means based on nature-inspired metaheuristic algorithms and identifying recent trends. The same authors presented different algorithms and optimizations which can be useful for the research community [25]. The K-Mean grouping method was used by [PERSON] et al. [26] to compare supermarkets based on various financial indicators. [PERSON] et al. [27] also used the K-Mean clustering method to compare insurance companies based on the ROE financial indicator. The K-Mean cluster analysis and principal component analysis were applied to classify EU-25 countries and provide a comparative view of the interplay between digital entrepreneur and sustainable development variables [28]. This method is considered the most popular clustering technique for partitioning a given dataset into a set of k-groups (clusters) [29]. Compared to K-Mean, the K-Medoid method works with representative values for each cluster.