zoomerjoin (version 0.2.0)

jaccard_string_group: Fuzzy String Grouping Using Minhashing

Description

Performs fuzzy string grouping in which similar strings are assigned to the same group. Uses the cluster_fast_greedy() community detection algorithm from the igraph package to create the groups. Must have igraph installed in order to use this function.

Usage

jaccard_string_group(
  string,
  n_gram_width = 2,
  n_bands = 45,
  band_width = 8,
  threshold = 0.7,
  progress = FALSE
)

Value

a string vector storing the group of each element in the original input strings. The input vector is grouped so that similar strings belong to the same group, which is given a standardized name.

Arguments

string

a character you wish to perform entity resolution on.

n_gram_width

the length of the n_grams used in calculating the jaccard similarity. For best performance, I set this large enough that the chance any string has a specific n_gram is low (i.e. n_gram_width = 2 or 3 when matching on first names, 5 or 6 when matching on entire sentences).

n_bands

the number of bands used in the minihash algorithm (default is 40). Use this in conjunction with the band_width to determine the performance of the hashing. The default settings are for a (.2,.8,.001,.999)-sensitive hash i.e. that pairs with a similarity of less than .2 have a >.1% chance of being compared, while pairs with a similarity of greater than .8 have a >99.9% chance of being compared.

band_width

the length of each band used in the minihashing algorithm (default is 8) Use this in conjunction with the n_bands to determine the performance of the hashing. The default settings are for a (.2,.8,.001,.999)-sensitive hash i.e. that pairs with a similarity of less than .2 have a >.1% chance of being compared, while pairs with a similarity of greater than .8 have a >99.9% chance of being compared.

threshold

the jaccard similarity threshold above which two strings should be considered a match (default is .95). The similarity is euqal to 1

• the jaccard distance between the two strings, so 1 implies the strings are identical, while a similarity of zero implies the strings are completely dissimilar.

progress

set to true to report progress of the algorithm

Examples

string <- c(
  "beniamino", "jack", "benjamin", "beniamin",
  "jacky", "giacomo", "gaicomo"
)
jaccard_string_group(string, threshold = .2, n_bands = 90, n_gram_width = 1)