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Theoretically Efficient and Practical Parallel DBSCAN

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Thank for your fast DBSCAN realization. I have a problem. Calling dbscan.DBSCAN(x) consums additional memory. If I call dbscan.DBSCAN(x) n time consums n*V memory, where V is memory for one...

With a 14178107*8 vector, a 108GB memory machine is quickly used up. Is there a way to reduce the memory footprint? The train output: ``` Input: 14178107 points, dimension 8...

Hi! Thank you again for the wonderful library. It seems that Numpy 2 is a restricted dependency. Would it be possible to recompile against Numpy 2? Thanks so much!

Just reporting that on Apple Silicon macs, I need to set os.environ["PARLAY_NUM_THREADS"] = "1" to avoid a kernel crash. Any higher values cause instability when using `dbscan` in quick succession....

Would you consider supporting more than 20 dimensions? This would be really useful for embeddings.