Comments (2)
bpkent
commented on August 19, 2024
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.
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deter3
commented on August 19, 2024
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 .
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Related Issues (16)
- User should be able to get a full branch-wise partition of the data from the LST. HOT 1
- Travis build is failing HOT 1
- Travis badge doesn't update with correct status
- knn density estimate produces overflow error for high-dimensional data HOT 2
- The readme doesn't indicate clearly which Python versions are supported HOT 1
- Package should include a (hidden) version number HOT 1
- Need a better error message for plot color nodes if a node is not valid HOT 1
- Pruning is buggy HOT 1
- Surprising behavior for `get_clusters` with the `first-k` method.
- How to sort nodes based on cluster density HOT 1
- end_level has huge number HOT 1
- Hitting overflow issues on sparse data HOT 1
- NetworkXError: SubGraph Views are readonly. Mutations not allowed HOT 3
- geom_tree import error HOT 2
- networkx problem
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