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

Public Sub New ( 
	Optional branching As Integer = 32,
	Optional iterations As Integer = 11,
	Optional centersInit As CenterInitType = CenterInitType.Random,
	Optional cbIndex As Single = 0.2F
)

public:
KMeansIndexParamses(
	int branching = 32, 
	int iterations = 11, 
	CenterInitType centersInit = CenterInitType::Random, 
	float cbIndex = 0.2f
)

new : 
        ?branching : int * 
        ?iterations : int * 
        ?centersInit : CenterInitType * 
        ?cbIndex : float32 
(* Defaults:
        let _branching = defaultArg branching 32
        let _iterations = defaultArg iterations 11
        let _centersInit = defaultArg centersInit CenterInitType.Random
        let _cbIndex = defaultArg cbIndex 0.2f
*)
-> KMeansIndexParamses

Parameters

branching (Optional)

Type: SystemInt32
The branching factor to use for the hierarchical k-means tree

iterations (Optional)

Type: SystemInt32
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 (Optional)

Type: Emgu.CV.FlannCenterInitType
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 (Optional)

Type: SystemSingle
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.

Reference