The KMeans type exposes the following members.
Gets or sets the cluster centroids. Setting this property is equivalent to setting KMeans.Clusters.Centroids.
Gets the clusters found by K-means.
Gets or sets whether covariance matrices for the clusters should be computed at the end of an iteration. Default is true.
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.
Gets the dimensionality of the data space.
Gets or sets the distance function used as a distance metric between data points.
Gets the cluster distortion error after the last call to this class' Compute methods.
Gets the number of iterations performed in the last call to this class' Compute methods.
Gets the number of clusters.
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.
Gets or sets the parallelization options for this algorithm.(Inherited from ParallelLearningBase.)
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.(Inherited from ParallelLearningBase.)
Gets or sets the relative convergence threshold for stopping the algorithm. Default is 1e-5.
Gets or sets the strategy used to initialize the centroids of the clustering algorithm. Default is KMeansPlusPlus.