KMeans Properties 
The KMeans type exposes the following members.
Name  Description  

Centroids 
Gets or sets the cluster centroids.
 
Clusters 
Gets the clusters found by Kmeans.
 
ComputeCovariances 
Gets or sets whether covariance matrices for the clusters should
be computed at the end of an iteration. Default is true.
 
ComputeError 
Gets or sets whether the clustering distortion error (the
average distance between all data points and the cluster
centroids) should be computed at the end of the algorithm.
The result will be stored in Error. Default is true.
 
Dimension 
Gets the dimensionality of the data space.
 
Distance 
Gets or sets the distance function used
as a distance metric between data points.
 
Error 
Gets the cluster distortion error after the
last call to this class' Compute methods.
 
Iterations 
Gets the number of iterations performed in the
last call to this class' Compute methods.
 
K 
Gets the number of clusters.
 
MaxIterations 
Gets or sets the maximum number of iterations to
be performed by the method. If set to zero, no
iteration limit will be imposed. Default is 0.
 
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)  
Tolerance 
Gets or sets the relative convergence threshold
for stopping the algorithm. Default is 1e5.
 
UseSeeding 
Gets or sets the strategy used to initialize the
centroids of the clustering algorithm. Default is
KMeansPlusPlus.
