huge memory being used

[deter3]

I did testing for 389 images with 400k 128 dimensions sift key points features .

It took over 10gb ram and used 15gb swap memory , eventually my ssd was out of storage and process went dead without error information .

I reduced to half of images with 200k 128 dimensions sift key points features . Same thing happened .
is there any solution I can fix it ?

[bpkent]

The current implementation is geared toward ease-of-use much more than scalability, so I'm afraid there isn't currently a great workaround. At the moment, the best way is to cluster your dataset from the top down and run DeBaCl on each cell of the partition.

[deter3]

Thanks for the reply . I figured DeBaCI clustering quality is better than HDBSCAN , and easier to manage , but just memory and time to process are the bottleneck .