scikit-learn / scikit-learn/scikit-learn
Add Kmeans parameter for pruning small clusters
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help wanted
Moderate
module:cluster
New Feature
- Dominant language
- Python
- Stars
- 67.3k
- Forks
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- Avg merge
- 1d 15h
- Merged PRs (30d)
- 58
Description
In Kmeans, often some clusters have only very little data. This might happen for all random initializations.
For this case, I would like to have an option to set a minimum cluster size, after which a cluster is dropped and a new one is created.
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 locating the Kmeans implementation and its existing tests in scikit-learn. Clarify how a minimum cluster size, dropped clusters, and replacement clusters should behave, then add tests defining the expected results before implementing the option.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100