The Bases of Association Rules of High Confidence
Abstract
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 in K. Adaricheva and J.B. Nation (TCS 2017), 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 K. Adaricheva et al. (Proceedings of ICFCA-2015). This paper focuses on the technical implementation of the new algorithm. Some testing results are performed on transaction data and medical data.
Keywords
Cite
@article{arxiv.1808.01703,
title = {The Bases of Association Rules of High Confidence},
author = {Oren Segal and Justin Cabot-Miller and Kira Adaricheva and J. B. Nation and Anuar Sharafudinov},
journal= {arXiv preprint arXiv:1808.01703},
year = {2018}
}
Comments
Presented at DTMN, Sydney, Australia, July 28, 2018