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- Bug Report: RuntimeWarning: invalid value encountered in scalar divide [\#112](https://github.com/firefly-cpp/NiaARM/issues/112)
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**Merged pull requests:**
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- Bump idna from 3.4 to 3.7 in /docs [\#126](https://github.com/firefly-cpp/NiaARM/pull/126) ([dependabot[bot]](https://github.com/apps/dependabot))
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- Bump idna from 3.4 to 3.7 [\#125](https://github.com/firefly-cpp/NiaARM/pull/125) ([dependabot[bot]](https://github.com/apps/dependabot))
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- Bump pillow from 10.2.0 to 10.3.0 in /docs [\#124](https://github.com/firefly-cpp/NiaARM/pull/124) ([dependabot[bot]](https://github.com/apps/dependabot))
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- Bump pillow from 10.2.0 to 10.3.0 [\#123](https://github.com/firefly-cpp/NiaARM/pull/123) ([dependabot[bot]](https://github.com/apps/dependabot))
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- Dependency version [\#122](https://github.com/firefly-cpp/NiaARM/pull/122) ([lahovniktadej](https://github.com/lahovniktadej))
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- docs: add rhododendrom as a contributor for design [\#120](https://github.com/firefly-cpp/NiaARM/pull/120) ([allcontributors[bot]](https://github.com/apps/allcontributors))
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- docs: add musicinmybrain as a contributor for doc [\#119](https://github.com/firefly-cpp/NiaARM/pull/119) ([allcontributors[bot]](https://github.com/apps/allcontributors))
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- docs: add lahovniktadej as a contributor for doc [\#118](https://github.com/firefly-cpp/NiaARM/pull/118) ([allcontributors[bot]](https://github.com/apps/allcontributors))
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- docs: add erkankarabulut as a contributor for code, and bug [\#117](https://github.com/firefly-cpp/NiaARM/pull/117) ([allcontributors[bot]](https://github.com/apps/allcontributors))
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- docs: add firefly-cpp as a contributor for code, bug, and 3 more [\#116](https://github.com/firefly-cpp/NiaARM/pull/116) ([allcontributors[bot]](https://github.com/apps/allcontributors))
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- docs: add zStupan as a contributor for code, bug, and 4 more [\#115](https://github.com/firefly-cpp/NiaARM/pull/115) ([allcontributors[bot]](https://github.com/apps/allcontributors))
- Fix division by zero error during acc calculation [\#113](https://github.com/firefly-cpp/NiaARM/pull/113) ([erkankarabulut](https://github.com/erkankarabulut))
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 integral 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](https://github.com/NiaOrg/NiaPy).
***Tested OS:** Windows, Ubuntu, Fedora, Alpine, Arch, macOS. **However, that does not mean it does not work on others**
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## Detailed insights
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## About 📋
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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 integral 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](https://github.com/NiaOrg/NiaPy). 🔗
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## Detailed insights 🔍
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The current version includes (but is not limited to) the following functions:
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- loading datasets in CSV format,
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- preprocessing of data,
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- searching for association rules,
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- providing output of mined association rules,
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- generating statistics about mined association rules,
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- visualization of association rules,
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- association rule text mining (experimental).
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- loading datasets in CSV format 📁
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- preprocessing of data 🧹
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- searching for association rules 🔎
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- providing output of mined association rules 📋
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- generating statistics about mined association rules 📊
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- visualization of association rules 📈
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- association rule text mining (experimental) 📄
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## Installation
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## Installation 📦
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### pip
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nix-env -iA nixos.python311Packages.niaarm
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```
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## Usage
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## Usage 🚀
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### Loading data
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print(data) # printing the dataset will generate a feature report
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```
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#### Option 2: From CSV file directly
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#### Option 2: Directly from a CSV file
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```python
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from niaarm import Dataset
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```
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Note: The CLI script can also run as a python module (`python -m niaarm ...`)
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## Reference Papers:
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## Reference Papers 📚
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Ideas are based on the following research papers:
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[6] I. Fister, I. Fister Jr., D. Novak and D. Verber, [Data squashing as preprocessing in association rule mining](https://iztok-jr-fister.eu/static/publications/300.pdf), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 1720-1725, doi: [10.1109/SSCI51031.2022.10022240](https://doi.org/10.1109/SSCI51031.2022.10022240).
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## See also
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[1][NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)
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[2][arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)
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## License
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This package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.
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## Cite us
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Stupan, Ž., & Fister Jr., I. (2022). [NiaARM: A minimalistic framework for Numerical Association Rule Mining](https://www.theoj.org/joss-papers/joss.04448/10.21105.joss.04448.pdf). Journal of Open Source Software, 7(77), 4448.
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## Contributors ✨
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Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
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<!-- ALL-CONTRIBUTORS-LIST:START - Do not remove or modify this section -->
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