LewisLabUCSD / LewisLabUCSD/Mito_Trace
Kmeans Clustering simulation results
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todo
- Dominant language
- Jupyter Notebook
- Stars
- 0
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
Feature: Kmeans and/or dynamic time and/or hierarchical clustering. Some may take longer than others.
- Kmeans clustering with fixed number of clusters
- Kmeans with elbow method to choose
Contributor guide
No contributing guide indexed for this repository
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 file, test, or entry point is named. Start by locating the existing Kmeans and elbow-method notebook work, then determine which of dynamic-time or hierarchical clustering and which simulation results remain; done requires an agreed scope and completed results for that scope.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, machine-learning
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100