[DOC] Hierarchical, spectral, or density-based clustering using sklearn and aeon distance metrics · Issue #1241 · aeon-toolkit/aeon · GitHub
More Web Proxy on the site http://driver.im/
You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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).
sklearn.cluster.SpectralClustering with affinity="precomputed" and the inverse of the distance matrix (large values indicate greater similarity)
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.
The text was updated successfully, but these errors were encountered:
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.
Hey,
I’m working on this issue and appreciate your guidance on a few points:-
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?
Are there any specific datasets or clustering algorithms you would like me to include in the examples (e.g., Agglomerative, Spectral Clustering)?
Is there a preferred format for the documentation (e.g., .md) or specific style guidelines I should follow?
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!
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).
sklearn.cluster.AgglomerativeClustering
withmetric="precomputed"
sklearn.cluster.DBSCAN
withmetric="precomputed"
sklearn.cluster.OPTICS
withmetric="precomputed"
sklearn.cluster.SpectralClustering
withaffinity="precomputed"
and the inverse of the distance matrix (large values indicate greater similarity)[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.
The text was updated successfully, but these errors were encountered: