The Bases of Association Rules of High Confidence
Oren Segal, Justin Cabot-Miller, Kira Adaricheva, Nation J.B, Anuar Sharafudinov · 2018
We develop a new approach for distributed computing of the association rules of high confidence in a binary table.It is derived from the D-basis algorithm [1], which is performed on multiple sub-tables of a table given by removing several rows at a time.The set of rules is then aggregated using the same approach as the D-basis is retrieved from a larger set of implications.This allows 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 using the relevance parameter introduced in [2].This paper focuses on the technical implementation of the new algorithm.Some testing results are performed on transaction data and medical data.