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These all look like clustering based on a 2d space, but does anyone know methods to tackle clustering on a network? Is it just a matter of tweaking the definit
by makmanalp 11y ago
These all look like clustering based on a 2d space, but does anyone know methods to tackle clustering on a network?
Is it just a matter of tweaking the definition of density / distance to the number of hops, or is it a different problem entirely? I can see how with 0 or 1 hops the data would be a very smushed distribution, versus 2d distance is much more rich and spread out.
- PeterisP 11y agoClustering algorithms generally only need a distance metric. Here 2d space is used for illustration just because it is easy to visualize. For n-dimensional space, geometric distance is often (but not always) used, but you can just use # of hops instead of that.
- makmanalp 11y agoBrilliant, that's what I figured, thank you!
- rm999 11y agoLooks at spectral clustering. Spectral methods usually take the distances between points to generate a graph, then operate directly on that graph. If you already have a graph/network, you can use those methods directly on it.
- lmcinnes 11y agoIf you want to use an HDBSCAN algorithm on graphs then I suggest you look into Spectral Clustering (which traditionally uses K-Means, but could use HDBSCAN instead. Otherwise you may want to consider graph specific algorithms such as Louvain.