Model for Finding Frequent Sets in FP-growth for Multimodal Data
Nataliya I. Boyko, Oleksandr Tkachyk · 2022
The paper examines the issues of hidden connections and potentially useful information from large data sets.Theoretical knowledge about associative rules is substantiated, their influence on connections in multimodal data sets is investigated.The methods of application of associative rules in practice are analyzed.The following are considered in detail: the basic concepts of associative rules and their connection with the idea of logical regularity; ways to determine the "strength" of these connections; basic algorithms for finding patterns; practical implementation of the search for associative rules.The regularities in the "templates" are analyzed: support and confidence value.The correct choice of these values, which directly affect the results of the search for rules, is experimentally determined.Research in this paper aimed to consider the basic concepts and find the Associative Rules both in traditional ways and in heterogeneous data of semantic networks, which creates specific problems when using existing algorithms.The data of semantic networks are analyzed, which in most cases serve a particular field and are highly specialized.The research presents the process of finding associative rules through the work of the classical Apriori algorithm and an alternative algorithm for finding associative rules.Previously, this problem was considered only to a small extent.The results of experiments on accurate SW data showed promising results.