partitions the ei into clusters of similar elements.


returns the vi corresponding to the ei in each cluster.


gives the same result.


returns the labeli corresponding to the ei in each cluster.


partitions data into at most n clusters.

Details and Options

  • FindClusters works for a variety of data types, including numerical, textual, and image, as well as dates and times.
  • The following options can be given:
  • CriterionFunctionAutomaticcriterion for selecting a method
    DistanceFunctionAutomaticthe distance function to use
    FeatureExtractorIdentityhow to extract features from which to learn
    FeatureNamesAutomaticfeature names to assign for input data
    FeatureTypesAutomaticfeature types to assume for input data
    MethodAutomaticwhat method to use
    PerformanceGoalAutomaticaspect of performance to optimize
    RandomSeeding1234what seeding of pseudorandom generators should be done internally
    WeightsAutomaticwhat weight to give to each example
  • By default, FindClusters will preprocess the data automatically unless a DistanceFunction is specified.
  • The setting for DistanceFunction can be any distance or dissimilarity function, or a function f defining a distance between two values.
  • Possible settings for PerformanceGoal include:
  • Automaticautomatic tradeoff among speed, accuracy, and memory
    "Quality"maximize the accuracy of the classifier
    "Speed"maximize the speed of the classifier
  • Possible settings for Method include:
  • Automaticautomatically select a method
    "Agglomerate"single-linkage clustering algorithm
    "DBSCAN"density-based spatial clustering of applications with noise
    "NeighborhoodContraction"displace examples toward high-density region
    "JarvisPatrick"JarvisPatrick clustering algorithm
    "KMeans"k-means clustering algorithm
    "MeanShift"mean-shift clustering algorithm
    "KMedoids"partitioning around medoids
    "SpanningTree"minimum spanning tree-based clustering algorithm
    "Spectral"spectral clustering algorithm
    "GaussianMixture"variational Gaussian mixture algorithm
  • The methods "KMeans" and "KMedoids" can only be used when the number of clusters is specified.
  • Possible settings for CriterionFunction include:
  • "StandardDeviation"root-mean-square standard deviation
    "Dunn"Dunn index
    "CalinskiHarabasz"CalinskiHarabasz index
    "DaviesBouldin"DaviesBouldin index
    Automaticinternal index
  • Possible settings for RandomSeeding include:
  • Automaticautomatically reseed every time the function is called
    Inheriteduse externally seeded random numbers
    seeduse an explicit integer or strings as a seed


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Basic Examples  (4)

Find clusters of nearby values:

Click for copyable input

Find exactly four clusters:

Click for copyable input

Represent clustered elements with the right-hand sides of each rule:

Click for copyable input

Represent clustered elements with the keys of the association:

Click for copyable input

Scope  (6)

Options  (15)

Applications  (3)

Properties & Relations  (2)

Neat Examples  (2)

Introduced in 2007
Updated in 2017