deepclustering is a curated reading list and resource collection for deep clustering research and related clustering methods.
The repository addresses the challenge of navigating the broad landscape of deep clustering literature by organizing papers and resources into a structured taxonomy. Rather than implementing clustering algorithms, the project serves as a lightweight entry point for researchers seeking a comprehensive overview of the field. It curates representative papers across multiple clustering paradigms and makes public codebases discoverable when available.
The tool suits researchers and practitioners who need to understand the state of deep clustering work across its various subdomains. It is particularly valuable for those new to the field who want a structured introduction rather than scattered searches. The repository's scope intentionally extends beyond canonical deep clustering to encompass related areas including multi-view clustering, graph clustering, subspace clustering, fairness considerations, optimal transport, and application-driven clustering methods. This breadth makes it useful for understanding how deep clustering connects to neighboring research areas.
The project maintains an organized collection of survey papers and categorized paper lists without implementing algorithms itself. Development activity shows consistent curation and updates to the resource collection, with the repository serving as a living reference that evolves as the field progresses. The inclusion of recently accepted survey papers indicates active engagement with the research community and responsiveness to emerging work in deep clustering.
