Eclat Association Rule is a Python implementation of the Eclat algorithm, a powerful data mining technique for discovering frequent itemsets in transaction databases. This project is developed by Imam Ilham Khawarizma and is released under the GNU General Public License, version 3.
Association rule mining is a fundamental concept in data mining, used to find relationships or patterns in data. Eclat (Equivalence Class Clustering and bottom-up Lattice Traversal) is one of the popular algorithms for finding frequent itemsets in transaction data. It's widely used in market basket analysis, recommendation systems, and more.
This implementation of Eclat can help you identify frequent itemsets and association rules within your datasets. It is based on the unlicensed dataset available from Kaggle, which can be found here.
You can use this Eclat Association Rule project to analyze your own transaction datasets, or you can use the provided reference script as a starting point. The reference script can be found at hands-on.cloud.
To get started, follow these steps:
- Clone or download this repository to your local machine.
- Install the required dependencies if not already installed.
- Use the provided script as a reference for implementing Eclat on your dataset.
- Tweak and customize the script to suit your specific data and requirements.
Feel free to contribute to this project or report any issues you encounter. Your contributions are welcome.
This project is licensed under the GNU General Public License, version 3. You can find the full license details in the LICENSE file.
I. Ilham Khawarizma
Special thanks to Kaggle for providing the dataset and hands-on.cloud for the reference script.
If you have any questions or need further assistance, please don't hesitate to reach out to the author or the project contributors.