Lackluster clustering performance
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Description
Hi! I tried following the clustering guide, but comparing tsne vs umap and k-means vs hdbscan, but in the end umap and hdbscan had some pretty awful performances, can you help me understand why?
The dataset used is [Imagenette](https://github.com/fastai/imagenette) (an easier subset of imagenet with only 10 classes) and I passed it through a pretrained resnet to get the features.
Link to the [colab](https://colab.research.google.com/drive/1viJLLGDBsEB1sAlXW8NPYjjXzgjx2lsk?usp=sharing)
Thanks in advance for the help
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Research direction
Start by running the linked Colab and reviewing the clustering guide, using the Imagenette features produced from the pretrained ResNet as the stated setup. Compare the reported t-SNE versus UMAP and k-means versus HDBSCAN results, then document a reproducible explanation for the performance difference or identify a confirmed issue.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100