Class KMeansIndexParams

When passing an object of this type the index constructed will be a hierarchical k-means tree.

public class KMeansIndexParams : UnmanagedObject, IDisposable, IIndexParams

Inherited Members: UnmanagedObject._ptr

UnmanagedObject.Ptr

DisposableObject.Dispose()

DisposableObject.ReleaseManagedResources()

object.GetType()

object.MemberwiseClone()

object.ToString()

object.Equals(object)

object.Equals(object, object)

object.ReferenceEquals(object, object)

object.GetHashCode()

Constructors

Initializes a new instance of the KMeansIndexParams class.

public KMeansIndexParams(int branching = 32, int iterations = 11, CenterInitType centersInit = CenterInitType.Random, float cbIndex = 0.2)

branching int: The branching factor to use for the hierarchical k-means tree
iterations int: The maximum number of iterations to use in the k-means clustering stage when building the k-means tree. A value of -1 used here means that the k-means clustering should be iterated until convergence
centersInit CenterInitType: The algorithm to use for selecting the initial centers when performing a k-means clustering step. The possible values are CENTERS_RANDOM (picks the initial cluster centers randomly), CENTERS_GONZALES (picks the initial centers using Gonzales’ algorithm) and CENTERS_KMEANSPP (picks the initial centers using the algorithm suggested in arthur_kmeanspp_2007 )
cbIndex float: This parameter (cluster boundary index) influences the way exploration is performed in the hierarchical kmeans tree. When cb_index is zero the next kmeans domain to be explored is chosen to be the one with the closest center. A value greater then zero also takes into account the size of the domain.

Release all the memory associated with this IndexParam

protected override void DisposeObject()