The concept of grouping (or clustering) data points with similar characteristics is of importance when working with the data that frequently appears in everyday life. Data scientists cluster the data that is numerical in nature based on the notion of distance, usually computed using Euclidean measure. However, there are many datasets that often consists of categorical values which require alternative methods for grouping the data. That is why clustering of categorical data employs methods that rely on similarity between the values rather than distance. This work focuses on studying the ability of different clustering algorithms and several definitions of similarity to organize categorical data into groups.