# uARMSolver: A framework for Association Rule Mining

arXiv: https://arxiv.org/abs/2010.10884 · HTML: https://mlsanity.com/paper/2010.10884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [A comprehensive review of visualization methods for association rule mining: Taxonomy, Challenges, Open problems and Future ideas](https://mlsanity.com/paper/2302.12594/) (2023)

## Abstract (arXiv)

The paper presents a novel software framework for Association Rule Mining named uARMSolver. The framework is written fully in C++ and runs on all platforms. It allows users to preprocess their data in a transaction database, to make discretization of data, to search for association rules and to guide a presentation/visualization of the best rules found using external tools. As opposed to the existing software packages or frameworks, this also supports numerical and real-valued types of attributes besides the categorical ones. Mining the association rules is defined as an optimization and solved using the nature-inspired algorithms that can be incorporated easily. Because the algorithms normally discover a huge amount of association rules, the framework enables a modular inclusion of so-called visual guiders for extracting the knowledge hidden in data, and visualize these using external tools.