Wolfram Language & System 11.0 (2016)|Legacy Documentation
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.
finds at most n clusters.
finds clusters at the specified level in array.
finds clusters of pixels with similar values in image.
finds at most n clusters in image.
- ClusteringComponents works for a variety of data types, including numerical, textual, and image, as well as dates and times.
- The following options can be given:
CriterionFunction Automatic criterion for selecting a method DistanceFunction Automatic the distance function to use Method Automatic what method to use PerformanceGoal Automatic aspect of performance to optimize Weights Automatic what weight to give to each example
- By default, the following distance functions are used for different types of elements:
ColorDistance colors EditDistance strings EuclideanDistance numeric data ImageDistance images JaccardDissimilarity Boolean data
- 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:
Automatic automatically 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" Jarvis–Patrick 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 "RSquared" R-squared "Dunn" Dunn index "CalinskiHarabasz" Calinski–Harabasz index "DaviesBouldin" Davies–Bouldin index Automatic internal index