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Find clusters in data—Wolfram Documentation

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• See Also
  • ArrayComponents
  • FindClusters
  • MorphologicalComponents
  • WatershedComponents
  • ClusteringTree
  • ClusteringMeasurements
  • ComponentMeasurements
• Related Guides
  • Segmentation Analysis
  • Cluster Analysis
  • Image Computation for Microscopy
  • 3D Images
  • Scientific Data Analysis
  • Video Analysis
  • Computational Photography
  • Text Analysis
  • Natural Language Processing
  • Unsupervised Machine Learning
  • Tabular Modeling

ClusteringComponents[array]

gives an array in which each element at the lowest level of array is replaced by an integer index representing the cluster in which the element lies.

• ClusteringComponents works for a variety of data types, including numerical, textual, and image, as well as dates and times.
• The number of clusters can be specified in the following ways:
• Automatic find the number of clusters automatically n find exactly n clusters UpTo[n] find at most n clusters
• The following options can be given:
• By default, ClusteringComponents 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:
• Automatic automatic 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:
• The methods "KMeans" and "KMedoids" can only be used when the number of clusters is specified.
• The methods "DBSCAN", "GaussianMixture", "JarvisPatrick", "MeanShift" and "NeighborhoodContraction" can only be used when the number of clusters is Automatic.
• The following plots show results of common methods on toy datasets:
• Possible settings for CriterionFunction include:
• "StandardDeviation" root-mean-square standard deviation "RSquared" R-squared "Dunn" Dunn index "CalinskiHarabasz" Calinski–Harabasz index "DaviesBouldin" Davies–Bouldin index Automatic internal index
• Possible settings for RandomSeeding include:
• Automatic automatically reseed every time the function is called Inherited use externally seeded random numbers seed use an explicit integer or string as a seed

Label two clusters of values in a list:

Label a vector of strings:

Cluster analysis of an MR image:

Clusters of values in a matrix:

Find color clusters in an image:

Find clusters in a 3D image:

Clustering transform of nested lists:

Find clusters at list level 2:

Find clusters at list level 1:

Find duplicates by specifying a large number of potential clusters:

Labeling clusters in a matrix:

Clustering lists of Booleans:

Clustering a list of Boolean vectors:

Generate some separated data and visualize it:

Find a cluster assignment with exactly two clusters using different settings for CriterionFunction:

Compare the two clusterings of the data:

By default, EditDistance is used to cluster a list of strings:

Use HammingDistance to cluster based on the number of characters that disagree:

Find clustering components for a list of images:

Create a custom FeatureExtractor to extract features:

Look at the resulting features:

Use the FeatureExtractor to find new clustering components:

Look at the new clustering:

Use FeatureNames to name features, and refer to their names in further specifications:

Use FeatureTypes to enforce the interpretation of the features:

Compare it to the result obtained by assuming nominal features:

Generate normally distributed data and visualize its histogram:

Find cluster assignments for this data using the "GaussianMixture" method:

Visualize the corresponding clustering:

Find cluster assignments for a list of string using the k-medoids method:

Look at the resulting clustering:

Find color clusters in an image using different methods:

Find color clusters in an image using the "NeighborhoodContraction" method and its suboption:

Find color clusters in an image using the "Spectral" method and its suboption:

Generate 500 random numerical vectors of length 1000:

Compute their clustering and benchmark the operation:

Perform the same operation with PerformanceGoal set to "Quality":

Generate 500 random numerical vectors in 2 dimensions:

Compute their clustering several times and compare the results:

Compute their clustering several times by changing the RandomSeeding option and compare the results:

Obtain cluster assignment for some numerical data:

Look at the cluster assignment when changing the weight given to each number:

Color segmentation of a microscopic image, after smoothing with a Perona–Malik filter:

Binary segmentation of an image:

The "KMeans" method cannot be used when the mean of a subset of the input does not belong to the input space:

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