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