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  • Extreme.Statistics.Multivariate
    • DendrogramNode Class
    • DistanceMeasures Class
    • Factor Class
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    • FactorCountMethod Enumeration
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    • HierarchicalCluster Class
    • HierarchicalClusterAnalysis Class
    • HierarchicalClusterCollection Class
    • KMeansCluster Class
    • KMeansClusterAnalysis Class
    • KMeansInitializationMethod Enumeration
    • LinearDiscriminantAnalysis Class
    • LinearDiscriminantFunction Class
    • LinkageMethod Enumeration
    • PartialLeastSquaresMethod Enumeration
    • PartialLeastSquaresModel Class
    • PrincipalComponent Class
    • PrincipalComponentAnalysis Class
    • PrincipalComponentCollection Class
    • ScalingMethod Enumeration
    • SimilarityMatrix Class
  • HierarchicalClusterAnalysis Class
    • HierarchicalClusterAnalysis Constructors
    • Properties
    • Methods

HierarchicalClusterAnalysis Class

Extreme Optimization Numerical Libraries for .NET Professional
Represents a hierarchical cluster analysis of a set of data.
Inheritance Hierarchy

SystemObject
  Extreme.DataAnalysis.ModelsModel
    Extreme.DataAnalysis.ModelsClusteringModelDouble
      Extreme.Statistics.MultivariateHierarchicalClusterAnalysis

Namespace:  Extreme.Statistics.Multivariate
Assembly:  Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
Syntax

C#
VB
C++
F#
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public class HierarchicalClusterAnalysis : ClusteringModel<double>
Public Class HierarchicalClusterAnalysis
	Inherits ClusteringModel(Of Double)
public ref class HierarchicalClusterAnalysis : public ClusteringModel<double>
type HierarchicalClusterAnalysis =  
    class
        inherit ClusteringModel<float>
    end

The HierarchicalClusterAnalysis type exposes the following members.

Constructors

  NameDescription
Public methodHierarchicalClusterAnalysis(IEnumerableVectorDouble)
Constructs a new HierarchicalClusterAnalysis.
Public methodHierarchicalClusterAnalysis(MatrixDouble)
Constructs a new HierarchicalClusterAnalysis.
Public methodHierarchicalClusterAnalysis(IDataFrame, String)
Constructs a new HierarchicalClusterAnalysis.
Public methodHierarchicalClusterAnalysis(IDataFrame, String)
Constructs a new HierarchicalClusterAnalysis.
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Properties

  NameDescription
Public propertyBaseFeatureIndex
Gets an index containing the keys of the columns that are required inputs to the model.
(Inherited from Model.)
Public propertyComputed Obsolete.
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyData
Gets an object that contains all the data used as input to the model.
(Inherited from Model.)
Public propertyDendrogramRoot
Gets the root node of the dendrogram.
Public propertyDistanceMeasure
Gets or sets the DistanceMeasure used to compute the distance between two cases.
Public propertyFitted
Gets whether the model has been computed.
(Inherited from Model.)
Public propertyInputSchema
Gets the schema for the features used for fitting the model.
(Inherited from Model.)
Public propertyLinkageMethod
Gets or sets the LinkageMethod for the analysis.
Public propertyMaxDegreeOfParallelism
Gets or sets the maximum degree of parallelism enabled by this instance.
(Inherited from Model.)
Public propertyModelSchema
Gets the collection of variables used in the model.
(Inherited from Model.)
Public propertyNumberOfObservations
Gets the number of observations the model is based on.
(Inherited from Model.)
Protected propertyParallelOptions
Gets or sets an object that specifies how the calculation of the model should be parallelized.
(Inherited from Model.)
Public propertySimilarityMatrix
Gets or sets the similarity matrix.
Public propertyStandardize
Gets or sets whether the variables should be standardized before the clustering is computed.
Public propertyStatus
Gets the status of the model, which determines which information is available.
(Inherited from Model.)
Public propertySupportsWeights
Indicates whether the model supports case weights.
(Inherited from Model.)
Public propertyWeights
Gets or sets the actual weights.
(Inherited from Model.)
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Methods

  NameDescription
Public methodCompute Obsolete.
Computes the model.
(Inherited from Model.)
Public methodCompute(ParallelOptions) Obsolete.
Computes the model.
(Inherited from Model.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodFit
Fits the model to the data.
(Inherited from Model.)
Public methodFit(ParallelOptions)
Fits the model to the data.
(Inherited from Model.)
Protected methodFitCore
Computes the model.
(Overrides ModelFitCore(ModelInput, ParallelOptions).)
Public methodGetClusterPartition
Returns the partition of the data into the specified number of clusters.
Public methodGetDendrogramOrder
Sorts the cases in an order suitable for displaying a dendrogram.
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodResetComputation Obsolete.
Clears all fitted model parameters.
(Inherited from Model.)
Public methodResetFit
Clears all fitted model parameters.
(Inherited from Model.)
Public methodSetDataSource
Uses the specified data frame as the source for all input variables.
(Inherited from Model.)
Public methodSummarize
Returns a string containing a human-readable summary of the object using default options.
(Inherited from Model.)
Public methodSummarize(SummaryOptions)
Returns a string containing a human-readable summary of the object using the specified options.
(Overrides ModelSummarize(SummaryOptions).)
Public methodToString
Returns a string that represents the current object.
(Inherited from Model.)
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Remarks

Use the HierarchicalClusterAnalysis to identify homogeneous subgroups of cases in a population using the agglomerative hierarchical clustering method. The method starts out by treating every case as its own cluster. It then successively combines ("agglomerates") clusters that are closest to each other until the entire population is one cluster. The order in which clusters are combined specifies a hierarchy. The last remaining clusters determine membership of the homogeneous groups.

The DistanceMeasure determines how the distance between clusters is calculated. The default is to use the squared Euclidean distance. The DistanceMeasures class defines several common distance measures.

The LinkageMethod property determines how cluster distances are updated when two clusters are merged. The default is the centroid method. The Standardize property determines whether the variables are transformed to all have the same mean and standard deviation. The default is .

Once the clustering has been computed by calling the Fit method, the GetClusterPartition(Int32) method can be used to partition the observations. This method returns a HierarchicalClusterCollection that provides detailed information about each of the clusters.

The results of a hierarchical cluster analysis are often presented in graphical form as a dendrogram, a tree-like structure that shows how clusters were combined. The DendrogramRoot property returns a DendrogramNode object that represents the root of the dendrogram. It provides all the information necessary to produce a dendrogram. See the DendrogramNode class for details.

See Also

Reference

Extreme.Statistics.Multivariate Namespace

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