public class KMeansClusterAnalysis : ClusteringModel

Public Class KMeansClusterAnalysis
	Inherits ClusteringModel

public ref class KMeansClusterAnalysis : public ClusteringModel

type KMeansClusterAnalysis =  
    class
        inherit ClusteringModel
    end

The KMeansClusterAnalysis type exposes the following members.

	Name	Description
	KMeansClusterAnalysis(IDataFrame, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(MatrixDouble, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(Matrix, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(NumericalVariable, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(IEnumerableVectorDouble, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(DataTable, String, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(IDataFrame, String, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(IDataFrame, String, Int32)	Constructs a new KMeansClusterAnalysis.
	KMeansClusterAnalysis(VariableCollection, String, Int32)	Constructs a new KMeansClusterAnalysis.

	Name	Description
	BaseFeatureIndex	Gets an index containing the keys of the columns that are required inputs to the model. (Inherited from Model.)
	Centers	Gets an array of cluster centers.
	Clusters	Gets an array of clusters.
	Computed	Gets whether the regression 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.)
	DependentVariables	Gets the collection of dependent variables associated with this model. (Inherited from Model.)
	IndependentVariables	Gets the collection of independent variables associated with this model. (Inherited from Model.)
	InitializationMethod	Gets or sets the method used to initialize the K-means algorithm.
	InputSchema	Gets the schema for the features used for fitting the model. (Inherited from Model.)
	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.)
	ModelSubset	Returns an array of indexes of final model features in the set of input features. (Inherited from Model.)
	NumberOfClusters	Gets or sets the number of clusters.
	NumberOfObservations	Gets the number of observations the model is based on. (Inherited from MultivariateModel.)
	ParallelOptions	Gets or sets an object that specifies how the calculation of the model should be parallelized. (Inherited from Model.)
	Predictions	Returns the index of the cluster each observation belongs to as a CategoricalVectorT.
	RandomNumberGenerator	Gets or sets the random number generator that may be used during the initialization of the K-means algorithm.
	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	Computes the model. (Inherited from Model.)
	Compute(ParallelOptions)	Computes the model. (Inherited from Model.)
	ComputeModel	Computes the model. (Inherited from Model.)
	ComputeModel(ParallelOptions)	Computes the clustering. (Overrides ModelComputeModel(ParallelOptions).)
	ComputeModel(ModelInput, ParallelOptions)	Computes the clustering. (Overrides ModelComputeModel(ModelInput, ParallelOptions).)
	Contains	Returns whether another UnivariateModel is nested within this instance. (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 to the specified input using the specified parallelization options. (Inherited from Model.)
	GetClusterDistances	Gets a matrix that contains the distances between the clusters.
	GetClusters	Returns an array containing the clusters in the model.
	GetDavisBouldinIndex	Returns the Davis-Bouldin index for the cluster analysis.
	GetDistancesToCenters	Returns a VectorT that contains the distance of each observation to its cluster center.
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetMemberships	Returns the cluster memberships as a CategoricalVariable.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ResetComputation	Clears all computed 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. (Inherited from Model.)
	ToString	Returns a string that represents the current object. (Inherited from Model.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

Use the KMeansClusterAnalysis class to perform a K-means cluster analysis of numerical data.

K-means clustering partitions a set of observations into a predefined number of clusters. A number of initial cluster centers is chosen. The observations are assigned to the closest cluster. Each center is then recalculated as the mean of its members. This changes the distances between cluster centers and observations, so the observations are once again reassigned. This process is repeated until no more observations change cluster. Note that the final partition depends on the initial location of the centers. Different applications may return different results for the same dataset.

The number of clusters must be passed in a constructor. It can be accessed through the NumberOfClusters property. The Standardize property determines whether the variables are transformed to all have the same mean and standard deviation. The default is .

Once the clustering is computed by calling the Compute(ParallelOptions) method, the Centers property gives access to a vector of cluster centers. The Predictions property returns a CategoricalVectorT that for each observation indicates the cluster it belongs to. It can be used to group the observations by their cluster assignment. GetDistancesToCenters returns a VectorT that contains the distance of each observation to the center of its cluster.

Numerical Libraries

Reference