HierarchicalClusterAnalysis Class

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.

	Name	Description
	HierarchicalClusterAnalysis(IEnumerableVectorDouble)	Constructs a new HierarchicalClusterAnalysis.
	HierarchicalClusterAnalysis(MatrixDouble)	Constructs a new HierarchicalClusterAnalysis.
	HierarchicalClusterAnalysis(IDataFrame, String)	Constructs a new HierarchicalClusterAnalysis.
	HierarchicalClusterAnalysis(IDataFrame, String)	Constructs a new HierarchicalClusterAnalysis.

	Name	Description
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	Computed	Obsolete. Gets whether the model has been computed. (Inherited from Model.)
	Data	Gets an object that contains all the data used as input to the model. (Inherited from Model.)
	DendrogramRoot	Gets the root node of the dendrogram.
	DistanceMeasure	Gets or sets the DistanceMeasure used to compute the distance between two cases.
	Fitted	Gets whether the model has been computed. (Inherited from Model.)
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	LinkageMethod	Gets or sets the LinkageMethod for the analysis.
	MaxDegreeOfParallelism	Gets or sets the maximum degree of parallelism enabled by this instance. (Inherited from Model.)
	ModelSchema	Gets the collection of variables used in the model. (Inherited from Model.)
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from Model.)
	ParallelOptions	Gets or sets an object that specifies how the calculation of the model should be parallelized. (Inherited from Model.)
	SimilarityMatrix	Gets or sets the similarity matrix.
	Standardize	Gets or sets whether the variables should be standardized before the clustering is computed.
	Status	Gets the status of the model, which determines which information is available. (Inherited from Model.)
	SupportsWeights	Indicates whether the model supports case weights. (Inherited from Model.)
	Weights	Gets or sets the actual weights. (Inherited from Model.)

	Name	Description
	Compute	Obsolete. Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Obsolete. Computes the model. (Inherited from Model.)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Fit	Fits the model to the data. (Inherited from Model.)
	Fit(ParallelOptions)	Fits the model to the data. (Inherited from Model.)
	FitCore	Computes the model. (Overrides ModelFitCore(ModelInput, ParallelOptions).)
	GetClusterPartition	Returns the partition of the data into the specified number of clusters.
	GetDendrogramOrder	Sorts the cases in an order suitable for displaying a dendrogram.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ResetComputation	Obsolete. Clears all fitted model parameters. (Inherited from Model.)
	ResetFit	Clears all fitted model parameters. (Inherited from Model.)
	SetDataSource	Uses the specified data frame as the source for all input variables. (Inherited from Model.)
	Summarize	Returns a string containing a human-readable summary of the object using default options. (Inherited from Model.)
	Summarize(SummaryOptions)	Returns a string containing a human-readable summary of the object using the specified options. (Overrides ModelSummarize(SummaryOptions).)
	ToString	Returns a string that represents the current object. (Inherited from Model.)

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.

Reference