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arXiv · 1808.01703

The Bases of Association Rules of High Confidence

Oren Segal·Justin Cabot-Miller·Kira Adaricheva·J. B. Nation·Anuar Sharafudinov
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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.

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Oren Segal, Justin Cabot-Miller, Kira Adaricheva, J. B. Nation, Anuar Sharafudinov. 2018-08-06. The Bases of Association Rules of High Confidence. https://doi.org/10.5121/csit.2018.81104

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