uARMSolver - universal Association Rule Mining Solver

Description

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.

Compiling

make

Run example

./bin/uARMSolver arm.set

arm.set is a problem definition file. Check README for more details about the format of .set file.

Reference Papers (software is based on ideas from):

[1] I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. Differential evolution for association rule mining using categorical and numerical attributes In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.

[2] I. Fister Jr., I Fister. Information cartography in association rule mining. arXiv preprint arXiv:2003.00348, 2020.

[3]

Contributors

Iztok Fister, Iztok Fister Jr.

Licence

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

Cite us

I. Fister, I Fister Jr. uARMSolver: A framework for Association Rule Mining. arXiv preprint arXiv:2010.10884, 2020.