universal Association Rule Mining Solver

🛠️ Compiling • 📦 Installation • 🚀 Example • 🐳 Docker • 📝 References • 📄 Cite us • 🔑 License • 🫂 Contributors

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

📦 Installation

To install uARMSolver on Fedora, use:

To install uARMSolver on RHEL, CentOS, Scientific Linux enable EPEL 8 and use:

To install uARMSolver on Arch-based distributions, use an AUR helper:

To install uARMSolver on Alpine Linux, enable Community repository and use:

To install uARMSolver on Windows, follow to the following instructions.

🚀 Example

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

🐳 Docker

If you prefer to use a Docker container for running uARMSolver, you can use the uarmsolver-container repository. This repository provides a Docker setup for running uARMSolver.

uARMSolver Container 📦

The uarmsolver-container repository contains a Docker container setup for running uARMSolver. You can find it here: uarmsolver-container.

To build and run the Docker container, follow the instructions in the uarmsolver-container README.

📝 References:

[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] I. Fister Jr., V. Podgorelec, I. Fister. Improved Nature-Inspired Algorithms for Numeric Association Rule Mining. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.

📄 Cite us

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

🔑 License

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!

🫂 Contributors

Iztok Fister, Iztok Fister Jr.