+
+ 
+ 
+
+ 
+
+
+ 
+
+
+ 
+
+
+ 
+
+ 
+ 
-# NiaARM - A minimalistic framework for Numerical Association Rule Mining
-
-[](https://pypi.python.org/pypi/niaarm)
-
-
-[](https://pepy.tech/project/niaarm)
-[](https://github.com/firefly-cpp/NiaARM/blob/main/LICENSE)
-
-[](http://isitmaintained.com/project/firefly-cpp/niaarm "Average time to resolve an issue")
-[](https://src.fedoraproject.org/rpms/python-niaarm)
-[](https://aur.archlinux.org/packages/python-niaarm)
-[](https://repology.org/project/python:niaarm/versions)
-[](https://doi.org/10.21105/joss.04448)
-[](#contributors-)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 
+
+
+ 🔍 Detailed insights • + 📦 Installation • + 🚀 Usage • + 📄 Cite us • + 📚 References • + 📖 See also • + 🔑 License • + 🫂 Contributors +
-## About 📋 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). 🔗 -## Detailed insights 🔍 +* **Documentation:** https://niaarm.readthedocs.io/en/latest +* **Tested OS:** Windows, Ubuntu, Fedora, Alpine, Arch, macOS. **However, that does not mean it does not work on others** + +## 🔍 Detailed insights The current version includes (but is not limited to) the following functions: - loading datasets in CSV format 📁 @@ -34,41 +81,41 @@ The current version includes (but is not limited to) the following functions: - visualization of association rules 📈 - association rule text mining (experimental) 📄 -## Installation 📦 +## 📦 Installation ### pip -Install NiaARM with pip: +To install `NiaARM` with pip, use: ```sh pip install niaarm ``` -To install NiaARM on Alpine Linux, please enable Community repository and use: +To install `NiaARM` on Alpine Linux, enable Community repository and use: ```sh $ apk add py3-niaarm ``` -To install NiaARM on Arch Linux, please use an [AUR helper](https://wiki.archlinux.org/title/AUR_helpers): +To install `NiaARM` on Arch Linux, use an [AUR helper](https://wiki.archlinux.org/title/AUR_helpers): ```sh $ yay -Syyu python-niaarm ``` -To install NiaARM on Fedora, use: +To install `NiaARM` on Fedora, use: ```sh $ dnf install python3-niaarm ``` -To install NiaARM on NixOS, please use: +To install `NiaARM` on NixOS, use: ```sh nix-env -iA nixos.python311Packages.niaarm ``` -## Usage 🚀 +## 🚀 Usage ### Loading data @@ -345,7 +392,11 @@ options: ``` Note: The CLI script can also run as a python module (`python -m niaarm ...`) -## Reference Papers 📚 +## 📄 Cite us + +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. + +## 📚 References Ideas are based on the following research papers: @@ -365,13 +416,13 @@ Ideas are based on the following research papers: [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). -## See also +## 📖 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) -## License +## 🔑 License This package is distributed under the MIT License. This license can be found online at
+ 
+ 
+ 
+