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Locality sensitive hashing (LSH) discovers potential matches among a corpus of documents quickly, so that only likely pairs can be compared.


lsh(x, bands, progress = interactive())



A TextReuseCorpus or TextReuseTextDocument.


The number of bands to use for locality sensitive hashing. The number of hashes in the documents in the corpus must be evenly divisible by the number of bands. See lsh_threshold and lsh_probability for guidance in selecting the number of bands and hashes.


Display a progress bar while comparing documents.


A data frame (with the additional class lsh_buckets), containing a column with the document IDs and a column with their LSH signatures, or buckets.


Locality sensitive hashing is a technique for detecting document similarity that does not require pairwise comparisons. When comparing pairs of documents, the number of pairs grows rapidly, so that only the smallest corpora can be compared pairwise in a reasonable amount of computation time. Locality sensitive hashing, on the other hand, takes a document which has been tokenized and hashed using a minhash algorithm. (See minhash_generator.) Each set of minhash signatures is then broken into bands comprised of a certain number of rows. (For example, 200 minhash signatures might be broken down into 20 bands each containing 10 rows.) Each band is then hashed to a bucket. Documents with identical rows in a band will be hashed to the same bucket. The likelihood that a document will be marked as a potential duplicate is proportional to the number of bands and inversely proportional to the number of rows in each band.

This function returns a data frame with the additional class lsh_buckets. The LSH technique only requires that the signatures for each document be calculated once. So it is possible, as long as one uses the same minhash function and the same number of bands, to combine the outputs from this function at different times. The output can thus be treated as a kind of cache of LSH signatures.

To extract pairs of documents from the output of this function, see lsh_candidates.


Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Mining of Massive Datasets (Cambridge University Press, 2011), ch. 3. See also Matthew Casperson, "Minhash for Dummies" (November 14, 2013).


dir <- system.file("extdata/legal", package = "textreuse")
minhash <- minhash_generator(200, seed = 235)
corpus <- TextReuseCorpus(dir = dir,
                          tokenizer = tokenize_ngrams, n = 5,
                          minhash_func = minhash)
buckets <- lsh(corpus, bands = 50)
#> Warning: `gather_()` was deprecated in tidyr 1.2.0.
#>  Please use `gather()` instead.
#>  The deprecated feature was likely used in the textreuse package.
#>   Please report the issue at <>.
#> # A tibble: 150 × 2
#>    doc          buckets                         
#>    <chr>        <chr>                           
#>  1 ca1851-match af692f79ec4b385468884a7310754366
#>  2 ca1851-match 7c25ad33c10068ed1d00e4a497be465e
#>  3 ca1851-match b36543167c07507579ecabbe47675f5f
#>  4 ca1851-match 68c1a9136b1aaf612b6982cf65db4dd9
#>  5 ca1851-match 99239407d33e84499e9b7fde16f43948
#>  6 ca1851-match 0ccad6c986d21807e9554b0757f6d156
#>  7 ca1851-match c32331c5c97d4d6b78fff25131aed561
#>  8 ca1851-match caac15d4cafc556ea08288ead841fd40
#>  9 ca1851-match d5868fcdf1f11f33859f640ac51e1931
#> 10 ca1851-match 999835f346cf6af6bf68bd96543997cd
#> # ℹ 140 more rows