On Apriori-Based Rule Generation and the Explainable Reasoning Functionality for Decision

Abstract

Using the Apriori-based method proposed by Agrawal for transactional data processing, we are proceeding with rule generation and construction of its execution environment from tabular data sets. This time, we will reconsider the functionality of decision-making by using the rules obtained. The application of rules to decision support is a long-standing problem, and we think that the need for it is increasing as a method to complement the recent black-boxing of conclusions in AI. Some IT companies have recently released software tools on “explainable AI” for black-boxing. In this paper, based on the presentation at FSS2020, we consider the functionality that can fully explain the conclusion using rules.