## 3 Sample, Dataset and Methods
2014): \"the main purpose of the technic is to group cases according to their degree of similarity\" and \"observations within a specific cluster are more homogeneous than observations between clusters\". This method assumes separating clusters containing the most similar elements of the considered chosen characteristics, which simultaneously differ as much as possible from the other elements ([PERSON] and [PERSON] 2017). The clusters are created based on the objects' mutual proximity (also distances or similarities), which may be estimated in many ways. The steps to be followed are data and variable selection, establishing the similarity measure, determining the cluster building method, selecting the clusters, and validating the results. ([PERSON] et al. 2014) indicated the three most common methods used in cluster analysis, which are correlation measures, distance measures (especially the Euclidean distance and its variants, such as squared distances) and association measures applicable for non-quantitative variables. In general, proximities may be determined directly or indirectly (more common due to [PERSON] et al. 2011). To calculate the distance, it is recommended to scale the data so that it falls into an interval 0-1 or is expressed as a percentage. There are various techniques to calculate the distances for binary and non-binary data having more value levels. Additionally, there are three main approaches to form clusters: hierarchical (applicable when there is no prior knowledge on how many clusters should be established), non-hierarchical (when the researcher knows the number of clusters, k-means are used) and a two-steps method (combined), where the number of clusters is determined using the first approach and then the outliers are assigned to given clusters using the second approach. Using a matrix of distances between the analysed elements, similarity-based clustering may be performed with the aim of grouping similar objects in one cluster and putting dissimilar objects in different ones ([PERSON] et al. 2015). ([PERSON] et al. 2011) emphasised the useful role of graphical presentations, which may suggest containing clusters of the observed data. Most commonly, histograms or scatterplots may be used. To form clusters, the authors applied the hierarchical approach, as the entry number of clusters was not known. After the number had been determined, the authors calculated the clusters' centers and distances of each element to the center in order to reclassify the elements, if necessary. The cluster's element should be reclassified if the distance of this element to its cluster's center is bigger than to another cluster's center ([PERSON] and [PERSON] 2004). The optimal grouping does not require any further reclassification. The initial grouping (subject to further reclassification) may be even arbitrary due to the post-checks. For this research, the ([PERSON] 1963) method is used, which entails the analysis of variance approach. It is one of the hierarchical approaches to clustering where the distance between clusters is calculated as the Euclidean distance between their centroids ([PERSON] 2020). Its goal is to minimise an increase in the total within-cluster error sum of squares, which is proportional to the squared Euclidean proximities between the centres of the formed clusters ([PERSON] et al. 2011). In this method the objects are combined to form one cluster containing all the analysed elements. If a merger of clusters minimises the increase in the total within-cluster error sum of squares, they are put together. To receive optimal clusters, the distance between the combined clusters should exceed an arbitrarily established threshold, which is the end of the cluster-combining procedure ([PERSON], 2020). This method is considered effective if the number of objects is not large ([PERSON], 2007; [PERSON] and [PERSON], 2017).