Jaro–Winkler distance: Difference between revisions

In computer science and statistics, the Jaro–Winkler distance is a string metric measuring an edit distance between two sequences. It is a variant proposed in 1990 by William E. Winkler of the Jaro distance metric (1989, Matthew A. Jaro).

The Jaro–Winkler distance uses a prefix scale ${\displaystyle p}$ which gives more favourable ratings to strings that match from the beginning for a set prefix length ${\displaystyle \ell }$.

The lower the Jaro–Winkler distance for two strings is, the more similar the strings are. The score is normalized such that 0 means an exact match and 1 means there is no similarity. The Jaro–Winkler similarity is the inversion, (1 − Jaro–Winkler distance).

Although often referred to as a distance metric, the Jaro–Winkler distance is not a metric in the mathematical sense of that term because it does not obey the triangle inequality.

The Jaro Similarity ${\displaystyle sim_{j}}$ of two given strings ${\displaystyle s_{1}}$ and ${\displaystyle s_{2}}$ is

${\displaystyle sim_{j}=\left\{{\begin{array}{l l}0&{\text{if }}m=0\\{\frac {1}{3}}\left({\frac {m}{|s_{1}|}}+{\frac {m}{|s_{2}|}}+{\frac {m-t}{m}}\right)&{\text{otherwise}}\end{array}}\right.}$

Where:

• ${\displaystyle |s_{i}|}$ is the length of the string ${\displaystyle s_{i}}$;
• ${\displaystyle m}$ is the number of matching characters (see below);
• ${\displaystyle t}$ is half the number of transpositions (see below).

Two characters from ${\displaystyle s_{1}}$ and ${\displaystyle s_{2}}$ respectively, are considered matching only if they are the same and not farther than ${\displaystyle \left\lfloor {\frac {\max(|s_{1}|,|s_{2}|)}{2}}\right\rfloor -1}$ characters apart.

Each character of ${\displaystyle s_{1}}$ is compared with all its matching characters in ${\displaystyle s_{2}}$. The number of matching (but different sequence order) characters divided by 2 defines the number of transpositions. For example, in comparing CRATE with TRACE, only 'R' 'A' 'E' are the matching characters, i.e. m=3. Although 'C', 'T' appear in both strings, they are farther apart than 1 (the result of ${\displaystyle \lfloor {\tfrac {5}{2}}\rfloor -1}$). Therefore, t=0 . In DwAyNE versus DuANE the matching letters are already in the same order D-A-N-E, so no transpositions are needed.

Jaro–Winkler similarity uses a prefix scale ${\displaystyle p}$ which gives more favorable ratings to strings that match from the beginning for a set prefix length ${\displaystyle \ell }$. Given two strings ${\displaystyle s_{1}}$ and ${\displaystyle s_{2}}$, their Jaro–Winkler similarity ${\displaystyle sim_{w}}$ is:

${\displaystyle sim_{w}=sim_{j}+\ell p(1-sim_{j}),}$

where:

• ${\displaystyle sim_{j}}$ is the Jaro similarity for strings ${\displaystyle s_{1}}$ and ${\displaystyle s_{2}}$
• ${\displaystyle \ell }$ is the length of common prefix at the start of the string up to a maximum of four characters
• ${\displaystyle p}$ is a constant scaling factor for how much the score is adjusted upwards for having common prefixes. ${\displaystyle p}$ should not exceed 0.25, otherwise the similarity could become larger than 1. The standard value for this constant in Winkler's work is ${\displaystyle p=0.1}$

The Jaro-Winkler distance ${\displaystyle d_{w}}$ is defined as ${\displaystyle d_{w}=1-sim_{w}}$.

Although often referred to as a distance metric, the Jaro–Winkler distance is not a metric in the mathematical sense of that term because it does not obey the triangle inequality.^[1] The Jaro–Winkler distance also does not satisfy the identity axiom ${\displaystyle d(x,y)=0\leftrightarrow x=y}$.

There are other popular measures of edit distance, which are calculated using a different set of allowable edit operations. For instance,

• the Levenshtein distance allows deletion, insertion and substitution;
• the Damerau–Levenshtein distance allows insertion, deletion, substitution, and the transposition of two adjacent characters;
• the longest common subsequence (LCS) distance allows only insertion and deletion, not substitution;
• the Hamming distance allows only substitution, hence, it only applies to strings of the same length.

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.

• Record linkage
• Census

• Cohen, W. W.; Ravikumar, P.; Fienberg, S. E. (2003). "A comparison of string distance metrics for name-matching tasks" (PDF). KDD Workshop on Data Cleaning and Object Consolidation. 3: 73–8.
• Jaro, M. A. (1989). "Advances in record linkage methodology as applied to the 1985 census of Tampa Florida". Journal of the American Statistical Association. 84 (406): 414–20. doi:10.1080/01621459.1989.10478785.
• Jaro, M. A. (1995). "Probabilistic linkage of large public health data file". Statistics in Medicine. 14 (5–7): 491–8. doi:10.1002/sim.4780140510. PMID 7792443.
• Winkler, W. E. (1990). "String Comparator Metrics and Enhanced Decision Rules in the Fellegi-Sunter Model of Record Linkage" (PDF). Proceedings of the Section on Survey Research Methods. American Statistical Association: 354–359.

• Winkler, W. E. (2006). "Overview of Record Linkage and Current Research Directions" (PDF). Research Report Series, RRS.

• strcmp.c - Original C implementation by the author of the algorithm