Different measures of distance or similarity are convenient for different types of analysis. The Wolfram Language provides built-in functions for many standard distance measures, as well as the capability to give a symbolic definition for an arbitrary measure.

Numerical Data

EuclideanDistance   ▪ SquaredEuclideanDistance   ▪ NormalizedSquaredEuclideanDistance   ▪ ManhattanDistance   ▪ ChessboardDistance   ▪ BrayCurtisDistance   ▪ CanberraDistance   ▪ CosineDistance   ▪ CorrelationDistance   ▪ BinaryDistance   ▪ WarpingDistance   ▪ CanonicalWarpingDistance

Boolean Data

HammingDistance   ▪ JaccardDissimilarity   ▪ MatchingDissimilarity   ▪ DiceDissimilarity   ▪ RogersTanimotoDissimilarity   ▪ RussellRaoDissimilarity   ▪ SokalSneathDissimilarity   ▪ YuleDissimilarity

String Data

EditDistance   ▪ DamerauLevenshteinDistance   ▪ HammingDistance   ▪ SmithWatermanSimilarity   ▪ NeedlemanWunschSimilarity

Images & Colors

ImageDistance   ▪ ColorDistance

Geospatial & Temporal Data

GeoDistance   ▪ TravelDistance   ▪ TravelTime   ▪ DateDifference

General & Mixed Data

FeatureDistance — distance in a space of features trained from data

DistanceFunction — option for specifying distance functions

DistanceMatrix — matrix of pairwise distances between elements

Nearest   ▪ FindClusters   ▪ MeanShift   ▪ FindShortestTour   ▪ SequenceAlignment   ▪ DistanceTransform   ▪ GraphDistance   ▪ CentralFeature

Visualization

Dendrogram   ▪ ClusteringTree

Related Tech Notes

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• Partitioning Data into Clusters

Related Guides

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• Sequence Alignment & Comparison
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• String Manipulation
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• Operations on Vectors
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• Hierarchical Clustering Package

Related Links

• Demonstrations related to Distance and Similarity Measures (Wolfram Demonstrations Project)