dedup
floc-simhash
dedup | floc-simhash | |
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1 | 1 | |
11 | 27 | |
- | - | |
0.0 | 0.0 | |
11 days ago | over 2 years ago | |
Python | Python | |
MIT License | MIT License |
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dedup
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[D] Hashing techniques to compare large datasets?
There is actually a whole family of hashing functions called locality sensitive hashing functions (LSH) that have the property that the likelihood of a hash collision is proportional to the similarity of the hashed data values. I’ve used Simhash myself for textual similarity, but LSHs can be used for finding similar images, audio, or other data types.
floc-simhash
What are some alternatives?
datasketch - MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
imagededup - 😎 Finding duplicate images made easy!
LSH - Locality Sensitive Hashing using MinHash in Python/Cython to detect near duplicate text documents
Name-That-Hash - 🔗 Don't know what type of hash it is? Name That Hash will name that hash type! 🤖 Identify MD5, SHA256 and 300+ other hashes ☄ Comes with a neat web app 🔥
dupeguru - Find duplicate files
MasquerBot - A TelegramBot for true paranoids.
hash-db - Create and selectively update a simple database of file hashes, sizes, and modification times.
deepdiff - DeepDiff: Deep Difference and search of any Python object/data. DeepHash: Hash of any object based on its contents. Delta: Use deltas to reconstruct objects by adding deltas together.