info.debatty.java.stringsimilarity

Class JaroWinkler

java.lang.Object

info.debatty.java.stringsimilarity.JaroWinkler

All Implemented Interfaces:

NormalizedStringDistance, NormalizedStringSimilarity, StringDistance, StringSimilarity, Serializable

@Immutable
public class JaroWinkler
extends Object
implements NormalizedStringSimilarity, NormalizedStringDistance

The Jaro–Winkler distance metric is designed and best suited for short strings such as person names, and to detect typos; it is (roughly) a variation of Damerau-Levenshtein, where the substitution of 2 close characters is considered less important then the substitution of 2 characters that a far from each other. Jaro-Winkler was developed in the area of record linkage (duplicate detection) (Winkler, 1990). It returns a value in the interval [0.0, 1.0]. The distance is computed as 1 - Jaro-Winkler similarity.

Author:

Thibault Debatty

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
JaroWinkler() Instantiate with default threshold (0.7).
JaroWinkler(double threshold) Instantiate with given threshold to determine when Winkler bonus should be used.

Method Summary

Methods

Modifier and Type	Method and Description
double	distance(String s1, String s2) Return 1 - similarity.
double	getThreshold() Returns the current value of the threshold used for adding the Winkler bonus.
double	similarity(String s1, String s2) Compute JW similarity.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

JaroWinkler

public JaroWinkler()

Instantiate with default threshold (0.7).

JaroWinkler

public JaroWinkler(double threshold)

Instantiate with given threshold to determine when Winkler bonus should be used. Set threshold to a negative value to get the Jaro distance.

Parameters:

threshold -

Method Detail

getThreshold

public final double getThreshold()

Returns the current value of the threshold used for adding the Winkler bonus. The default value is 0.7.

Returns:

the current value of the threshold

similarity

public final double similarity(String s1,
                String s2)

Compute JW similarity.

Specified by:

similarity in interface StringSimilarity

Parameters:

s1 -

s2 -

Returns:

distance

public final double distance(String s1,
              String s2)

Return 1 - similarity.

Specified by:

distance in interface StringDistance

Parameters:

s1 -

s2 -

Returns: