Jaccard Similarity for Sets with strings
Jaccard Similarity (J) = (number of matching strings present in both sets) / (number of strings in either of the set)
Considering A and B as two sets, it can be represented in symbolic form as
J(A, B) = |A Ո B| / |A U B|
Example :
Let A and B be two sets of strings where
Set A = { ‘John’, ‘is’, ’going’,’ to’, ’the’, ’market’, ’today’, ’to’, ’buy’, ’cake’} and
Set B = {‘Tim’, ‘is’, ‘at’, ’the’,’ shop’, ’already’, ’for’, ‘buying’, ‘two’, ‘cakes’}
Find Jaccard Similarity between the two sets.
R
# Install package "bayesbio" and load the library library(bayesbio) # Two strings "String_A" and "String_B" as sets String_A < - c("John", "is", "going", "to", "the", "market", "today", "to", "buy", "cake") String_B < - c("Tim", "is", "at", "the", "shop", "already", "for", "buying", "two", "cakes") # Computing Jaccard similarity between strings word # by word # Note - value 0 denotes complete match and 1 denotes # no match as per "stringdist" function stringdist(String_A, String_B, method='jaccard') # Computing Jaccard similarity between strings overall jaccardSets(String_A, String_B) # Computing Jaccard distance jaccard_distance = 1 - jaccardSets(String_A, String_B) jaccard_distance |
Output
How to Calculate Jaccard Similarity in R?
Jaccard Similarity also called as Jaccard Index or Jaccard Coefficient is a simple measure to represent the similarity between data samples. The similarity is computed as the ratio of the length of the intersection within data samples to the length of the union of the data samples.
It is represented as –
J(A, B) = |A Ո B| / |A U B|
It is used to find the similarity or overlap between the two binary vectors or numeric vectors or strings. It can be represented as J. There is also a closely related term associated with Jaccard Similarity which is called Jaccard Dissimilarity or Jaccard Distance. Jaccard Distance is a measure of dissimilarity between data samples and can be represented as (1 – J) where J is Jaccard Similarity.
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