KMeansTData, TDataSum Constructor

public KMeans(
	Func<TDataSum> createDefaultFunction,
	Func<TDataSum, TData, double> distanceFunction,
	bool isDistanceFunctionReturningSquareOfDistance,
	Func<TDataSum, TData, TDataSum> sumUpFunction,
	Func<TDataSum, int, TDataSum> divideFunction
)

Parameters

createDefaultFunction  FuncTDataSum

Creates the default data. Sum up the default data and other data should result in the same value of the other data.

distanceFunction  FuncTDataSum, TData, Double

A function that evaluates the distance between the mean value of the cluster and a data point. The return value is either the Euclidean distance, or the square of the distance. In the latter case the next argument must be set to true.

isDistanceFunctionReturningSquareOfDistance  Boolean

True if the distanceFunction returns the square of the distance.

sumUpFunction  FuncTDataSum, TData, TDataSum

A function that adds the first and the second argument and returns the result.

divideFunction  FuncTDataSum, Int32, TDataSum

A function that divides the first argument by the second argument. Calling this function signals that the sum up is finished. Because of this extra functionality, the divide function is called even if the divisor is 1.

For taking the linear mean, the sumUpFunction can be defined as

sumUpFunction = (sum, x) => sum + x

divideFunction = (nom, denom) => nom/denom.

For taking the logarithmic mean, the sumUpFunction can be defined as

sumUpFunction = (sum, x) => sum + Math.Log(x)

divideFunction = (nom, denom) => Math.Exp(nom/denom)

Reference