Oren Segal¹, Justin Cabot-Miller², Kira Adaricheva², J.B.Nation³ and Anuar Sharafudinov⁴, ^1,2Hofstra University, USA, ³University of Hawaii, USA and ⁴AILabs, Kazakhstan

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.

Association rules, implication, binary table, D-basis, parallel computing