scikit-learn / scikit-learn/scikit-learn
Adding Yinyang K-Means implementation
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- Dominant language
- Python
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Description
The proposed Yinyang K-Means algorithm modification offers a significant speedup while keeping giving exactly the same results (compared to regular K-Means).
Here’s the original paper presented at ICML’15: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/ding15.pdf
@amueller kindly informed me that @MechCoder has mentioned it somewhere and probably has started implementation.
@MechCoder, can you please give an update?
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
No files, tests, or entry points are named. Start by reading the linked ICML’15 Yinyang K-Means paper and locating the existing regular K-Means implementation; done means an implementation that provides a speedup while producing exactly the same results as regular K-Means.
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
- 20/100