NiaARM - A minimalistic framework for numerical association rule mining.

General outline of the framework

NiaARM is a framework for Association Rule Mining based on nature-inspired algorithms for optimization. The framework is written fully in Python and runs on all platforms. NiaARM allows users to preprocess the data in a transaction database automatically, to search for association rules and provide a pretty output of the rules found. This framework also supports numerical and real-valued types of attributes besides the categorical ones. Mining the association rules is defined as an optimization problem, and solved using the nature-inspired algorithms that come from the related framework called NiaPy.

Detailed insights

The current version witholds (but is not limited to) the following functions:

• loading datasets in CSV format,
• preprocessing of data,
• searching for association rules,
• providing output of mined association rules,
• generating statistics about mined association rules.

Installation

pip3

Install NiaARM with pip3:

pip3 install niaarm

Examples

For a list of examples see the examples folder.

Reference Papers:

Ideas are based on the following research papers:

[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., 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.

[3] I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (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!