This repository provides a comprehensive collection of methods and implementations for deep clustering, targeting researchers and practitioners in machine learning and data mining. It aims to offer a unified platform for exploring, comparing, and developing advanced clustering techniques, particularly those leveraging deep learning architectures.

How It Works

The project focuses on deep clustering, a paradigm that integrates deep neural networks with traditional clustering algorithms. This approach allows for learning powerful, non-linear representations of data that are more amenable to clustering, often leading to improved performance over methods relying solely on handcrafted features. The repository showcases a variety of deep clustering architectures, including autoencoders, variational autoencoders, and contrastive learning frameworks, applied to diverse data types like images, graphs, and text.

Quick Start & Requirements

• Primary install: The README does not specify a primary installation command (e.g., pip, Docker). Users are directed to individual sub-directories for specific implementations.
• Prerequisites: Implementations are primarily in PyTorch, with some in TensorFlow, Keras, Theano, Caffe, Lua, and MATLAB. Specific dependencies will vary by method.
• Resources: No specific resource requirements are listed.

Highlighted Details

• Features a survey paper on deep clustering accepted by ACM Computing Surveys.
• Includes implementations for various deep clustering methods, often with links to original papers and code.
• Covers data-type-specific clustering, such as Deep Graph Clustering.
• Lists a wide range of deep clustering papers from major conferences and journals.

Maintenance & Community

• The repository is actively updated, with recent additions from 2024.
• Links to research papers and associated code are provided, facilitating community engagement.
• No specific community channels (e.g., Discord, Slack) are mentioned.

Licensing & Compatibility

• The repository itself does not specify a license. Individual code implementations may have their own licenses.
• Compatibility for commercial use or closed-source linking would depend on the licenses of the individual methods included.

Limitations & Caveats

The repository is a curated collection of research implementations rather than a single, unified library. Users will need to navigate individual sub-directories and manage dependencies for each method, which may vary significantly. Some listed methods do not have readily available code.