This project implements a document clustering solution that automatically groups documents on the same topic into corresponding folders. The clustering algorithm processes textual data, identifies similarities between documents, and organizes them into distinct clusters based on their content. This tool is ideal for efficiently managing large collections of documents by ensuring that related files are stored together. The project is built using Python and leverages machine learning techniques for accurate topic-based clustering.
- Automatic Topic Detection: The algorithm identifies the main topics within a set of documents and clusters them accordingly.
- Folder Organization: Automatically creates and organizes documents into topic-specific folders, making it easy to manage large collections.