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KMeans—Wolfram Language Documentation

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• See Also
  • FindClusters
  • ClusterClassify
  • ClusteringComponents
  • ClusteringTree
  • Dendrogram
  • DimensionReduction
  • Methods
  • Agglomerate
  • GaussianMixture
  • JarvisPatrick
  • KMedoids
  • MeanShift
  • NeighborhoodContraction
  • SpanningTree
  • Spectral
• Tech Notes
  • Partitioning Data into Clusters

• Method for FindClusters, ClusterClassify and ClusteringComponents.
• Partitions data into a specified clusters of similar elements using a k-means clustering algorithm.

Find exactly four clusters of nearby values using the "KMeans" clustering method:

Create random 2D vectors:

Plot computed clusters using the "KMeans" method:

Train a ClassifierFunction on a list of strings:

Find the cluster assignments and gather the elements by their cluster:

Cluster data using Manhattan distance:

Generate a list of 100 random colors:

Cluster the colors without specifying the initial configuration of centroids using the "KMeans" method:

Specify the initial colors to be used as centroids using the "KMeans" method:

Create random 2D vectors:

Find different clusterings of data using the "KMeans" method by varying the "InitialCentroids":

Create and visualize noisy 2D moon-shaped training and test datasets:

Train a ClassifierFunction using "KMeans" for two clusters and find clusters in the test set:

Visualizing clusters indicates that "KMeans" performs poorly on intertwined clusters:

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