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Diffstat (limited to 'README.md')

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1 files changed, 22 insertions, 14 deletions

@@ -48,6 +48,7 @@ Some notable algorithms to consider are: - simhash (aka, Charikar hashing) - minhash - weighed minhash +- shingle methods (with dozens of feature hashes per document) However, the papers describing these algorithms have a few parameters (eg, hash bit length, token hash algorithm), and leave several important steps up to the @@ -110,34 +111,39 @@ Proposed extraction and tokenization: - specifically, no zero-width or non-printing unicode modifiers - numbers (unless part of an alphanumeric string, eg an acronym) should not be included -- TODO: instead, strip all numeric characters? +- TODO: instead, just strip all numeric characters? - OPTIONALLY, a language-specific stop-list appropriate for search-engine indexing may be used. Proposed algorithm and parameters: - `simhash` algorithm -- TODO: fast token hashing algorithm? -- TODO: 64x signed 8-bit buckets during calculation -- 64bit length final form -- base32 (non-capitalization-sensitive) when serialized as a string -- TODO: little-endian? does base32 specify this? +- 64-bit `jenkins` hash algorithm for tokens +- 64x signed buckets (of 64-bit depth) during calculation +- 64-bit final form +- base32 when serialized as a string (non-capitalization-sensitive) -Recommended lookup parameters: +Note: endianness does not need to be specified, as the 64bit feature hashes are +defined as 8-byte octet strings, not 64-bit integers. -- TODO: how many columns in rotation? -- k=3 (number of bits to filter on) -- TODO: Hamming distance -- TODO: 0.XYZ considered a loose match, 0.XYZ considered a close match +Recommended default lookup parameters (these are all optional): + +- k=3 ("distance", or number of differing bits to allow when filtering) +- Hamming distance as a similarity metric (for speed) +- 0.90 considered a loose match, 0.98 considered a close match +- create 16 tables of 28-bits for lookup, by first chosing 1 out of 4 16-bit + blocks, then subdividing the remaining 48 bits into 4 12-bit blocks and + choosing one of them, as described in Manku et al 3.1.1. ## Existing Libraries and Implementations These don't necessarily implement the schemes above, but they do implement the basic algorithms. -- [seomoz/simhash-cpp](https://github.com/seomoz/simhash-cpp) (C++): simhash, - used in production for web de-dupe -- [simhash](https://github.com/JanX2/simhash) (C++): Google's simhash +- [seomoz/simhash-cpp](https://github.com/seomoz/simhash-cpp) (C++) and + [seomoz/simhash-py](https://github.com/seomoz/simhash-py) (python, wrapping + the C++ library): simhash, used in production for web de-duplication +- [simhash](https://github.com/JanX2/simhash) (C++): mirror of Google's simhash implementation (old?) - [datasketch](https://ekzhu.github.io/datasketch/) (Python): minhash, Jaccard similarity, minhash-based indexing with Jaccard threshold, Top-K, or @@ -166,6 +172,8 @@ Other resources: Proceedings of the thiry-fourth annual ACM symposium on Theory of computing, ACM Press, 2002" +> The original simhash paper + "Manku, Jain, Sarma: Detecting Near-Duplicates for Web Crawling". in Proceedings of the 16th international conference on World Wide Web, ACM Press, 2007 |