Represents a K-Means cluster analysis.
SystemObject Extreme.DataAnalysis.ModelsModel Extreme.DataAnalysis.ModelsClusteringModelDouble Extreme.Statistics.MultivariateKMeansClusterAnalysis
Namespace:
Extreme.Statistics.Multivariate
Assembly:
Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
public class KMeansClusterAnalysis : ClusteringModel<double>
Public Class KMeansClusterAnalysis
Inherits ClusteringModel(Of Double)
public ref class KMeansClusterAnalysis : public ClusteringModel<double>
type KMeansClusterAnalysis =
class
inherit ClusteringModel<float>
end
The KMeansClusterAnalysis type exposes the following members.
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| 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 | 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.) |
 | Fitted |
Gets whether the model has been computed.
(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.) |
 | NumberOfClusters |
Gets or sets the number of clusters.
|
 | 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.) |
 | Predictions | |
 | 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.) |
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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 Fit
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.
Reference