lsh_probability: Probability that a candidate pair will be detected with LSH
In ropensci/textreuse: Detect Text Reuse and Document Similarity

Description Usage Arguments Details References Examples

Functions to help choose the correct parameters for the lsh and minhash_generator functions. Use lsh_threshold to determine the minimum Jaccard similarity for two documents for them to likely be considered a match. Use lsh_probability to determine the probability that a pair of documents with a known Jaccard similarity will be detected.

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lsh_probability(h, b, s)

lsh_threshold(h, b)

`h`	The number of minhash signatures.
`b`	The number of LSH bands.
`s`	The Jaccard similarity.

Locality sensitive hashing returns a list of possible matches for similar documents. How likely is it that a pair of documents will be detected as a possible match? If h is the number of minhash signatures, b is the number of bands in the LSH function (implying then that the number of rows r = h / b), and s is the actual Jaccard similarity of the two documents, then the probability p that the two documents will be marked as a candidate pair is given by this equation.

p = 1 - (1 - s^{r})^{b}

According to MMDS, that equation approximates an S-curve. This implies that there is a threshold (t) for s approximated by this equation.

t = \frac{1}{b}^{\frac{1}{r}}

Jure Leskovec, Anand Rajaraman, and Jeff Ullman, Mining of Massive Datasets (Cambridge University Press, 2011), ch. 3.

# Threshold for default values
lsh_threshold(h = 200, b = 40)

# Probability for varying values of s
lsh_probability(h = 200, b = 40, s = .25)
lsh_probability(h = 200, b = 40, s = .50)
lsh_probability(h = 200, b = 40, s = .75)