<p align="center">
<img alt="logo" width="500" src=".github/images/NiaAutoARM.png">
</p>
# NiaAutoARM
A novel AutoML method for automatically constructing the full association rule mining pipelines based on stochastic population-based metaheuristics.
## About
The numerical association rule mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets that consist of both features. The process is not considered as easy since it incorporates several components that form an entire pipeline, i.e., preprocessing, algorithm selection, hyperparameter optimization, and the definition of metrics that evaluate the quality of the association rule. NiaAutoARM software aims to automatize this process and reduce the need for the user's effort to discover association rules.
## How it works?
See the following [preprint](https://arxiv.org/pdf/2501.00138) for more information.
## Installation 📦
### pip
Install NiaARM with pip:
```sh
pip install niaautoarm
```
## Usage 🚀
## See also
[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)
[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)
## References
[1] Ž. Stupan, 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.
[2] L. Pečnik, Fister, I., Fister, I. Jr. [NiaAML2: An Improved AutoML Using Nature-Inspired Algorithms](https://doi.org/10.1007/978-3-030-78811-7_23). In International Conference on Swarm Intelligence (pp. 243-252). Springer, Cham, 2021.
## 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!
Raw data
{
"_id": null,
"home_page": "https://github.com/firefly-cpp/NiaAutoARM",
"name": "NiaAutoARM",
"maintainer": null,
"docs_url": null,
"requires_python": "<4.0,>=3.9",
"maintainer_email": null,
"keywords": "association rule mining, computational intelligence, data mining, datasets, optimization",
"author": "Uro\u0161 Mlakar",
"author_email": "uros.mlakar@um.si",
"download_url": "https://files.pythonhosted.org/packages/68/c1/f928d77d778df4e85ccfdbe921e5cba3d26c17c0170597929396b228a3d1/niaautoarm-0.1.1.tar.gz",
"platform": null,
"description": "<p align=\"center\">\n <img alt=\"logo\" width=\"500\" src=\".github/images/NiaAutoARM.png\">\n</p>\n\n# NiaAutoARM\n\nA novel AutoML method for automatically constructing the full association rule mining pipelines based on stochastic population-based metaheuristics.\n\n## About\n\nThe numerical association rule mining paradigm that includes concurrent dealing with numerical and categorical attributes is beneficial for discovering associations from datasets that consist of both features. The process is not considered as easy since it incorporates several components that form an entire pipeline, i.e., preprocessing, algorithm selection, hyperparameter optimization, and the definition of metrics that evaluate the quality of the association rule. NiaAutoARM software aims to automatize this process and reduce the need for the user's effort to discover association rules.\n\n## How it works?\n\nSee the following [preprint](https://arxiv.org/pdf/2501.00138) for more information.\n\n## Installation \ud83d\udce6\n\n### pip\n\nInstall NiaARM with pip:\n\n```sh\npip install niaautoarm\n```\n## Usage \ud83d\ude80\n\n## See also\n\n[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)\n\n[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)\n\n## References\n[1] \u017d. Stupan, 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.\n\n[2] L. Pe\u010dnik, Fister, I., Fister, I. Jr. [NiaAML2: An Improved AutoML Using Nature-Inspired Algorithms](https://doi.org/10.1007/978-3-030-78811-7_23). In International Conference on Swarm Intelligence (pp. 243-252). Springer, Cham, 2021.\n\n## License\nThis package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.\n\n## Disclaimer\nThis 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!\n",
"bugtrack_url": null,
"license": null,
"summary": "Automated generation and evaluation of Association Rule Mining pipelines",
"version": "0.1.1",
"project_urls": {
"Homepage": "https://github.com/firefly-cpp/NiaAutoARM",
"Repository": "https://github.com/firefly-cpp/NiaAutoARM"
},
"split_keywords": [
"association rule mining",
" computational intelligence",
" data mining",
" datasets",
" optimization"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "06b811562ebe4364a0311827fa10aaea499d1694fdc2558ff76a8e34fe31eb86",
"md5": "4f65fbd90dec8634dba58bdd5c8f6db1",
"sha256": "5edfc33ec06f74e0a6a24b255482b0cf95b72bb4f7b7475b454fa7b9ef442984"
},
"downloads": -1,
"filename": "niaautoarm-0.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "4f65fbd90dec8634dba58bdd5c8f6db1",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<4.0,>=3.9",
"size": 11489,
"upload_time": "2025-01-14T13:25:18",
"upload_time_iso_8601": "2025-01-14T13:25:18.857932Z",
"url": "https://files.pythonhosted.org/packages/06/b8/11562ebe4364a0311827fa10aaea499d1694fdc2558ff76a8e34fe31eb86/niaautoarm-0.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "68c1f928d77d778df4e85ccfdbe921e5cba3d26c17c0170597929396b228a3d1",
"md5": "cac1057414c257b04e5ecef42b278896",
"sha256": "6505746ed89d0ceff7d0da099816986b4aab345bbf3c7d884f1779fe0ffa4381"
},
"downloads": -1,
"filename": "niaautoarm-0.1.1.tar.gz",
"has_sig": false,
"md5_digest": "cac1057414c257b04e5ecef42b278896",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<4.0,>=3.9",
"size": 9943,
"upload_time": "2025-01-14T13:25:20",
"upload_time_iso_8601": "2025-01-14T13:25:20.046553Z",
"url": "https://files.pythonhosted.org/packages/68/c1/f928d77d778df4e85ccfdbe921e5cba3d26c17c0170597929396b228a3d1/niaautoarm-0.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-01-14 13:25:20",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "firefly-cpp",
"github_project": "NiaAutoARM",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "niaautoarm"
}
JSON
