Determination of regional variants in the versification of Estonian folksongs using an interpretable fuzzy rule-based classifier

In this paper, a method of hierarchical clustering and a selection of fuzzy classification algorithms are applied successively to the data set that contains measured characteristics of folk verses collected from 104 historical parishes of Estonia. The aim of the study is to detect the groups of parishes that are similar in terms of folk verse characteristics and to give us insight into the reasoning that the separation into these groups is based upon. The process of classification separates the initial groups into further subsets represented by fuzzy rules, which can be analyzed thanks to the interpretability of such rules. To emphasize the latter, most important features in individual rules are brought out by rule compression. The results of the analysis are backed by what is known from linguistic sciences.