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I am trying to find clusters in some data with high noise (see plot below, data here).

enter image description here

I tried using DBSCAN which sort of worked, but it required quite a bit of manually tuning the input parameters to find the clusters properly. Are there any other good clustering algorithms for dealing with this kind of data?

Some considerations:

  • I am using Julia to do my data processing.

  • The data has periodic boundary conditions in both directions.

  • The number of clusters is known a priori.

  • I am planning to process many datasets in this way, so it should run relatively fast and not require too much manual fiddling.

Thanks!

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It could be interesting to reduce noise (=smoothing) as much as possible before applying a clustering algorithm.

Furthermore, periodic boundaries that have too many values may alter results, and that's why it could be a good option to simplify values when it is possible.

If non of the previous options are possible, you could apply point density measures to just keep the zones with high density.

Therefore, you can apply a grid of hexagons to just take hexagons with a high density of points, or kernel density estimation.

See also:

https://juliapackages.com/p/hexagons

https://github.com/JuliaStats/KernelDensity.jl

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  • $\begingroup$ Does it answer your question? If not, please let me know. $\endgroup$ Sep 9, 2022 at 10:21

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