NiaARM documentation!

NiaARM is 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 includes (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,

  • visualization of association rules,

  • association rule text mining (experimental).

Documentation

The main documentation is organized into a couple of sections:

References

[1]

Iztok Fister, Andres Iglesias, Akemi Galvez, Javier Del Ser, Eneko Osaba, and Iztok Fister. Differential Evolution for Association Rule Mining Using Categorical and Numerical Attributes. In Hujun Yin, David Camacho, Paulo Novais, and Antonio J. Tallón-Ballesteros, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2018, 79–88. Cham, 2018. Springer International Publishing. doi:10.1007/978-3-030-03493-1_9.

[2]

Iztok Fister Jr., Vili Podgorelec, and Iztok Fister. Improved Nature-Inspired Algorithms for Numeric Association Rule Mining. In Pandian Vasant, Ivan Zelinka, and Gerhard-Wilhelm Weber, editors, Intelligent Computing and Optimization, 187–195. Cham, 2021. Springer International Publishing. doi:10.1007/978-3-030-68154-8_19.

[3]

Iztok Fister Jr. and Iztok Fister. A brief overview of swarm intelligence-based algorithms for numerical association rule mining. arXiv:2010.15524 [cs], October 2020. doi:10.48550/ARXIV.2010.15524.

[4]

Iztok Fister, Suash Deb, and Iztok Fister. Population-based metaheuristics for Association Rule Text Mining. In Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, ISMSI '20, 19–23. New York, NY, USA, March 2020. Association for Computing Machinery. doi:10.1145/3396474.3396493.

[5]

Iztok Fister, Dušan Fister, Andres Iglesias, Akemi Galvez, Eneko Osaba, Javier Del Ser, and Iztok Fister. Visualization of Numerical Association Rules by Hill Slopes. In Cesar Analide, Paulo Novais, David Camacho, and Hujun Yin, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2020, 101–111. Cham, 2020. Springer International Publishing. doi:10.1007/978-3-030-62362-3_10.