From Wikipedia, the free encyclopedia  View original article
It has been suggested that Damerau–Levenshtein distance be merged into this article. (Discuss) Proposed since November 2013. 
This article needs additional citations for verification. (February 2010) 
In information theory and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of singlecharacter edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.^{[1]}
Levenshtein distance may also be referred to as edit distance, although that may also denote a larger family of distance metrics.^{[2]}^{:32} It is closely related to pairwise string alignments.
Mathematically, the Levenshtein distance between two strings is given by where
where is the indicator function equal to 0 when and equal to 1 otherwise.
Note that the first element in the minimum corresponds to deletion (from to ), the second to insertion and the third to match or mismatch, depending on whether the respective symbols are the same.
For example, the Levenshtein distance between "kitten" and "sitting" is 3, since the following three edits change one into the other, and there is no way to do it with fewer than three edits:
The Levenshtein distance has several simple upper and lower bounds. These include:
In approximate string matching, the objective is to find matches for short strings in many longer texts, in situations where a small number of differences is to be expected. The short strings could come from a dictionary, for instance. Here, one of the strings is typically short, while the other is arbitrarily long. This has a wide range of applications, for instance, spell checkers, correction systems for optical character recognition, and software to assist natural language translation based on translation memory.
The Levenshtein distance can also be computed between two longer strings, but the cost to compute it, which is roughly proportional to the product of the two string lengths, makes this impractical. Thus, when used to aid in fuzzy string searching in applications such as record linkage, the compared strings are usually short to help improve speed of comparisons.
There are other popular measures of edit distance, which are calculated using a different set of allowable edit operations. For instance,
Edit distance is usually defined as a parameterizable metric calculated with a specific set of allowed edit operations, and each operation is assigned a cost (possibly infinite). This is further generalized by DNA sequence alignment algorithms such as the Smith–Waterman algorithm, which make an operation's cost depend on where it is applied.
This is a straightforward, but inefficient, recursive pseudocode implementation of a LevenshteinDistance
function that takes two strings, s and t, together with their lengths, and returns the Levenshtein distance between them:
// len_s and len_t are the number of characters in string s and t respectively int LevenshteinDistance(string s, int len_s, string t, int len_t) { /* base case: empty strings */ if (len_s == 0) return len_t; if (len_t == 0) return len_s; /* test if last characters of the strings match */ if (s[len_s1] == t[len_t1]) cost = 0; else cost = 1; /* return minimum of delete char from s, delete char from t, and delete char from both */ return minimum(LevenshteinDistance(s, len_s  1, t, len_t ) + 1, LevenshteinDistance(s, len_s , t, len_t  1) + 1, LevenshteinDistance(s, len_s  1, t, len_t  1) + cost); }
Unfortunately, this straightforward recursive implementation is very inefficient because it recomputes the Levenshtein distance of the same substrings many times.
A more efficient method would never repeat the same distance calculation. For example, the Levenshtein distance of all possible prefixes might be stored in an array d[][]
where d[i][j]
is the distance between the first i
characters of string s
and the first j
characters of string t
. The table is easy to construct one row at a time starting with row 0. When the entire table has been built, the desired distance is d[len_s][len_t]
. While this technique is significantly faster, it will consume len_s * len_t
more memory than the straightforward recursive implementation.
Computing the Levenshtein distance is based on the observation that if we reserve a matrix to hold the Levenshtein distances between all prefixes of the first string and all prefixes of the second, then we can compute the values in the matrix in a dynamic programming fashion, and thus find the distance between the two full strings as the last value computed.
This algorithm, an example of bottomup dynamic programming, is discussed, with variants, in the 1974 article The Stringtostring correction problem by Robert A. Wagner and Michael J. Fischer.^{[3]}
This is a straightforward pseudocode implementation for a function LevenshteinDistance that takes two strings, s of length m, and t of length n, and returns the Levenshtein distance between them:
int LevenshteinDistance(char s[1..m], char t[1..n]) { // for all i and j, d[i,j] will hold the Levenshtein distance between // the first i characters of s and the first j characters of t; // note that d has (m+1)*(n+1) values declare int d[0..m, 0..n] clear all elements in d // set each element to zero // source prefixes can be transformed into empty string by // dropping all characters for i from 1 to m { d[i, 0] := i } // target prefixes can be reached from empty source prefix // by inserting every character for j from 1 to n { d[0, j] := j } for j from 1 to n { for i from 1 to m { if s[i] = t[j] then d[i, j] := d[i1, j1] // no operation required else d[i, j] := minimum ( d[i1, j] + 1, // a deletion d[i, j1] + 1, // an insertion d[i1, j1] + 1 // a substitution ) } } return d[m, n] }
Note that this implementation does not fit the definition precisely: it always prefers matches, even if insertions or deletions provided a better score. This is equivalent; it can be shown that for every optimal alignment (which induces the Levenshtein distance) there is another optimal alignment that prefers matches in the sense of this implementation.^{[4]}
Two examples of the resulting matrix (hovering over a number reveals the operation performed to get that number):


The invariant maintained throughout the algorithm is that we can transform the initial segment s[1..i]
into t[1..j]
using a minimum of d[i,j]
operations. At the end, the bottomright element of the array contains the answer.
It turns out that only two rows of the table are needed for the construction if one does not want to reconstruct the edited input strings (the previous row and the current row being calculated).
The Levenshtein distance may be calculated iteratively using the following algorithm:^{[5]}
int LevenshteinDistance(string s, string t) { // degenerate cases if (s == t) return 0; if (s.Length == 0) return t.Length; if (t.Length == 0) return s.Length; // create two work vectors of integer distances int[] v0 = new int[t.Length + 1]; int[] v1 = new int[t.Length + 1]; // initialize v0 (the previous row of distances) // this row is A[0][i]: edit distance for an empty s // the distance is just the number of characters to delete from t for (int i = 0; i < v0.Length; i++) v0[i] = i; for (int i = 0; i < s.Length; i++) { // calculate v1 (current row distances) from the previous row v0 // first element of v1 is A[i+1][0] // edit distance is delete (i+1) chars from s to match empty t v1[0] = i + 1; // use formula to fill in the rest of the row for (int j = 0; j < t.Length; j++) { var cost = (s[i] == t[j]) ? 0 : 1; v1[j + 1] = Minimum(v1[j] + 1, v0[j + 1] + 1, v0[j] + cost); } // copy v1 (current row) to v0 (previous row) for next iteration for (int j = 0; j < v0.Length; j++) v0[j] = v1[j]; } return v1[t.Length]; }
The Wikibook R_Programming has a page on the topic of: Levenshtein distance in R 
The Wikibook Algorithm implementation has a page on the topic of: Levenshtein distance 