[FEA] Support Minibatch Kmeans Clustering
Open
Nobody has claimed this yet.
improvement
- Dominant language
- Cuda
- Stars
- 854
- Forks
- 236
- Avg merge
- 3d 3h
- Merged PRs (30d)
- 62
Description
MiniBatchKMeans is a scalable variant of the classic K-Means clustering algorithm. It uses small, random subsets (mini-batches) of the data in each iteration.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reading the linked scikit-learn MiniBatchKMeans documentation and the cuVS clustering area. The issue does not name files, entry points, tests, or acceptance criteria, so confirm the intended API, algorithm scope, and validation approach with maintainers before starting.
Written by the indexing model from the issue text.
Assessment
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100