8000 [DOC] Hierarchical, spectral, or density-based clustering using sklearn and aeon distance metrics · Issue #1241 · aeon-toolkit/aeon · GitHub
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[DOC] Hierarchical, spectral, or density-based clustering using sklearn and aeon distance metrics #1241

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SebastianSchmidl opened this issue Feb 24, 2024 · 4 comments
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@SebastianSchmidl
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SebastianSchmidl commented Feb 24, 2024

Describe the issue linked to the documentation

The clustering component in aeon currently supports only partition-based methods. However, there are also hierarchical, spectral, and density-based clustering methods [1].

Suggest a potential alternative/fix

Using the distance metrics in aeon, we can pre-compute the distance matrix for traditional clustering methods. Some methods are already implemented in sklearn, which is a core dependency of eaon and, thus, available to users. I think we should at least link to the sklearn-clusterers in the documentation. With a bit more effort, we could provide examples on how to use sklearn's clusterers with aeon's distance measures (here).

I did not yet test this approach.

[1]: Paparrizos, John, and Luis Gravano. "Fast and Accurate Time-Series Clustering." ACM Transactions on Database Systems 42, no. 2 (2017): 8:1-8:49. https://doi.org/10.1145/3044711.

@SebastianSchmidl SebastianSchmidl added the documentation Improvements or additions to documentation label Feb 24, 2024
@TonyBagnall
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thanks for this, we have some examples I think of using precomputed with scikit, but if its not clear it would be great if it was clearer. I would like to get density peaks in, iirc we have a java implementation.

@TonyBagnall TonyBagnall added the good first issue Good for newcomers label Jun 8, 2024
@SalmanDeveloperz
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Hey, Can i work on this issue?

@SebastianSchmidl
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Yes, sure.
@aeon-actions-bot assign @SalmanDeveloperz

@aeon-actions-bot aeon-actions-bot bot removed the good first issue Good for newcomers label Jan 4, 2025
@SalmanDeveloperz
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Hey,
I’m working on this issue and appreciate your guidance on a few points:-

  1. Where should I add the example? Should it go in an existing documentation file (if so, which one), or should I create a new file in the docs/ directory?
  2. Are there any specific datasets or clustering algorithms you would like me to include in the examples (e.g., Agglomerative, Spectral Clustering)?
  3. Is there a preferred format for the documentation (e.g., .md) or specific style guidelines I should follow?
  4. Should I include the example code in a separate script or keep it embedded within the documentation file?

Once I have clarification, I’ll proceed with the implementation and submit a PR.
Thank you for your guidance!

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