Generate some separated data and visualize it:

Cluster the data using different settings for CriterionFunction:

Compare the two clusterings of the data:

Use CanberraDistance as the measure of distance for continuous data:

Clusters obtained with the default SquaredEuclideanDistance:

Use DiceDissimilarity as the measure of distance for Boolean data:

Use MatchingDissimilarity as the measure of distance for Boolean data:

Use HammingDistance as the measure of distance for string data:

Define a distance function as a pure function:

Find clusters for a list of images:

Create a custom FeatureExtractor to extract features:

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:

Cluster the data hierarchically:

Clusters obtained with the default method:

Generate normally distributed data and visualize it:

Cluster the data in 4 clusters by using the k-means method:

Cluster the data using the "GaussianMixture" method without specifying the number of clusters:

Generate some uniformly distributed data:

Cluster the data in 2 clusters by using the k-means method:

Cluster the data using the "DBSCAN" method without specifying the number of clusters:

Generate a list of colors:

Cluster the colors in 5 clusters using the k-medoids method:

Cluster the colors without specifying the number of clusters using the "MeanShift" method:

Cluster the colors without specifying the number of clusters using the "NeighborhoodContraction" method:

Cluster the colors using the "NeighborhoodContraction" method and its suboptions:

Generate 500 random numerical vectors of length 1000:

Compute their clustering and benchmark the operation:

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

Generate 500 random numerical vectors in two 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: