This repository contains an implementation of the Locality Sensitive Hashing (LSH) algorithm as a part of the CS 476 Data Mining course project.

Locality Sensitive Hashing is a technique used for approximate nearest neighbor search in high-dimensional data. It aims to efficiently identify similar items by mapping them to the same "bucket" with high probability. This implementation provides a scalable and efficient solution for similarity search in large datasets.

• LSH Algorithm: Implements the Locality Sensitive Hashing technique.
• Hash Functions: Includes various hash functions such as random projection, min-hash, or permutation-based hashing.
• Similarity Search: Performs approximate nearest neighbor search using LSH for efficient similarity matching.
• Modularity: The implementation is designed with a modular approach, allowing easy customization and experimentation with different components.

To get started with this implementation, follow these steps:

1. Clone the repository:

git clone https://github.com/ardaeerol/LSH.git

2. Install the necessary dependencies:

cd LSH
pip install -r requirements.txt

4. Customize the LSH parameters and hash functions according to your specific use case.

5. Run the main script to perform similarity search on your dataset:

python main.py

Contributions to this project are welcome. If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.

This project is licensed under the MIT License