The D-basis Algorithm for Association Rules of High Confidence

Abstract

We develop a new approach for distributed computing of the association rules of high confidence on the attributes/columns of a binary table. It is derived from the D-basis algorithm developed by K.Adaricheva and J.B.Nation (Theoretical Computer Science, 2017), which runs multiple times on sub-tables of a given binary table, obtained by removing one or more rows. The sets of rules retrieved at these runs are then aggregated. This allows us to obtain a basis of association rules of high confidence, which can be used for ranking all attributes of the table with respect to a given fixed attribute. This paper focuses on some algorithmic details and the technical implementation of the new algorithm. Results are given for tests performed on random, synthetic and real data.