Package org.apache.commons.text.similarity

Provides algorithms for string similarity.

See: Description

Interface Summary

Interface	Description
EditDistance<R>	Interface for Edit Distances.
SimilarityScore<R>	Interface for the concept of a string similarity score.

Class Summary

Class	Description
CosineDistance	Measures the cosine distance between two character sequences.
CosineSimilarity	Measures the Cosine similarity of two vectors of an inner product space and compares the angle between them.
EditDistanceFrom<R>	This stores a EditDistance implementation and a CharSequence "left" string.
FuzzyScore	A matching algorithm that is similar to the searching algorithms implemented in editors such as Sublime Text, TextMate, Atom and others.
HammingDistance	The hamming distance between two strings of equal length is the number of positions at which the corresponding symbols are different.
JaccardDistance	Measures the Jaccard distance of two sets of character sequence.
JaccardSimilarity	Measures the Jaccard similarity (aka Jaccard index) of two sets of character sequence.
JaroWinklerDistance	A similarity algorithm indicating the percentage of matched characters between two character sequences.
LevenshteinDetailedDistance	An algorithm for measuring the difference between two character sequences.
LevenshteinDistance	An algorithm for measuring the difference between two character sequences.
LevenshteinResults	Container class to store Levenshtein distance between two character sequences.
LongestCommonSubsequence	A similarity algorithm indicating the length of the longest common subsequence between two strings.
LongestCommonSubsequenceDistance	An edit distance algorithm based on the length of the longest common subsequence between two strings.
SimilarityScoreFrom<R>	This stores a SimilarityScore implementation and a CharSequence "left" string.

Package org.apache.commons.text.similarity Description

Provides algorithms for string similarity.

The algorithms that implement the EditDistance interface follow the same simple principle: the more similar (closer) strings are, lower is the distance. For example, the words house and hose are closer than house and trousers.

The following algorithms are available at the moment:

• Cosine Distance
• Cosine Similarity
• Fuzzy Score
• Hamming Distance
• Jaro-Winkler Distance
• Levenshtein Distance

The Cosine Distance utilises a regular expression tokenizer (\w+). And the Levenshtein Distance's behaviour can be changed to take into consideration a maximum throughput.

Since:

1.0