partitions the ei into clusters of similar elements.
returns the vi corresponding to the ei in each cluster.
gives the same result.
returns the labeli corresponding to the ei in each cluster.
partitions data into at most n clusters.
Details and Options
- FindClusters 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 FeatureExtractor Identity how to extract features from which to learn FeatureNames Automatic feature names to assign for input data FeatureTypes Automatic feature types to assume for input data Method Automatic what method to use PerformanceGoal Automatic aspect of performance to optimize RandomSeeding 1234 what seeding of pseudorandom generators should be done internally Weights Automatic what weight to give to each example
- By default, FindClusters will preprocess the data automatically unless a DistanceFunction is specified.
- 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
- Possible settings for RandomSeeding include:
Automatic automatically reseed every time the function is called Inherited use externally seeded random numbers seed use an explicit integer or strings as a seed
Examplesopen allclose all
Basic Examples (4)
Properties & Relations (2)
Neat Examples (2)
- Logic & Boolean Algebra
- Boolean Computation
- Cluster Analysis
- Life Sciences & Medicine: Data & Computation
- Text Analysis
- Scientific Data Analysis
- Sequence Alignment & Comparison
- Statistical Data Analysis
- Distance and Similarity Measures
- Handling Arrays of Data
- Computer Vision
- Computational Geometry
- Numerical Data
- Machine Learning