COMPARATIVE ANALYSIS OF THE EVALUATION OF THE CONVERGENCE OF DATA STRUCTURES USING MATHEMATICAL SETS AND THE LEVENSHTEIN ALGORITHM

his paper describes a comparison of two methods for evaluating the convergence of different data sets: mathematical sets and the Levenshtein algorithm. The first part of the paper describes a general methodology for evaluating the convergence of data structures, including the steps of analyzing, cleaning, and constructing data sets. The stages of assessing data convergence with a common structure are presented. The goal of assessing data convergence with a common structure is to determine how well a model or theory explains the observed data. To improve data convergence metrics, the Levenshtein algorithm was selected from a set of algorithms based on semantic search. In the second part of the paper, a comparative analysis of the two methods is performed using several data sets as examples. Before performing data convergence calculations, preliminary data cleaning was carried out using standard data mining techniques implemented in the Python programming language. In addition, normalization of fields within the data structure was performed, which was particularly important when working with relational databases to ensure accurate interpretation and comparability of the results. The results of the comparative analysis of the presented methods are given and a number of recommendations are given for choosing the most effective method for evaluating the convergence of data structures depending on specific requirements and conditions