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: