uARMSolver: A framework for Association Rule Mining
Abstract
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.
Cite
@article{arxiv.2010.10884,
title = {uARMSolver: A framework for Association Rule Mining},
author = {Iztok Fister and Iztok Fister},
journal= {arXiv preprint arXiv:2010.10884},
year = {2020}
}