Comments (1)
ekzhu
commented on June 15, 2024
Hi Navein. By similarity matrix you mean pair-wise Jaccard similarity score for every pair of sets? If your goal is to have the exact similarity scores, then this package cannot help you.
If you are okay with approximate Jaccard similarity scores, then you can create a MinHash for each set, and compute all pairs using the MinHashes. This should be faster than computing the exact scores, if the sets are mostly larger than the number of hash values used in MinHash.
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Related Issues (20)
- Advice for compression of a big graph HOT 3
- Distributed MinHashLSH HOT 3
- Poor default args in MinHashLSH? HOT 1
- Is is possible to rename already created index? HOT 1
- Add C-minHash variant HOT 11
- Synchronous Mongodb Storage HOT 3
- Merging (Identically Specified) MinHashLSH objects HOT 11
- Impact of MinHashLSH threshold on memory usage HOT 2
- Too large minhashLSH index HOT 10
- Is the bumber of bands correct? HOT 3
- Choice of np.uint64? HOT 11
- def jaccard 's denominator is self not [self union other] . HOT 2
- How to Use MinHash and MinHashLSH to Identify Comprehensive Documents and Partial Matches? HOT 3
- Forever growing index HOT 4
- HNSW: `HNSW.add` will not set the entry point of new levels HOT 2
- Process-safe, no mem bloat, implementation of LSH HOT 1
- Implementing MinHash retrieval from keys for MinHashLSHForest HOT 2
- Cassandra storage not compatible with Python 3.12 HOT 5
- Question: Effects of Bit Truncation on MinhashLSH? HOT 15
- uint64 overflow risk
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