Wolfram Language & System 10.4 (2016)|Legacy Documentation
This is documentation for an earlier version of the Wolfram Language.View current documentation (Version 11.2)
- ClusterClassify 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 between speed, accuracy, and memory "Memory" minimize the storage requirements of the classifier "Quality" maximize the accuracy of the classifier "Speed" maximize the speed of the classifier "TrainingSpeed" minimize the time spent producing 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
- The methods , , and 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
Train the ClassifierFunction on some numerical data:
Train the ClassifierFunction on some colors by requiring the number of classes to be 5:
Train the ClassifierFunction on some unlabeled data:
Train the ClassifierFunction on some strings:
Introduced in 2016