KMeansTData, TDataSum Class

Clusters data using the KMeans algorithm.

Inheritance Hierarchy

SystemObject
Altaxo.Calc.ClusteringKMeansTData, TDataSum
Altaxo.Calc.ClusteringKMeans_Double1D

Namespace: Altaxo.Calc.Clustering
Assembly: AltaxoCore (in AltaxoCore.dll) Version: 4.8.3179.0 (4.8.3179.0)

Syntax

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public class KMeans<TData, TDataSum>

Type Parameters

TData: Type of the data.
TDataSum: Type of data that is used to calculate the mean by summing up and dividing by the cluster count.

The KMeansTData, TDataSum type exposes the following members.

Constructors

	Name	Description
	KMeansTData, TDataSum	Initalize a new instance of KMeansTData, TDataSum.

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Properties

	Name	Description
	ClusterCounts	Gets a list which contains the number of values in each of the clusters (length is numberOfClusters).
	ClusterIndices	Gets a list with the same length as the number of data points, in which each element is the index of the cluster this data point is assigned to.
	ClusterMeans	Gets a list which contains the mean values of the clusters (length is numberOfClusters).
	Data	Gets a list which contains the data points provided.
	HasPatchingEmptyClustersFailed	If true, during evaluation, empty clusters have appeared, which could not be patched with other data.
	HasReachedMaximumNumberOfIterations	If true, the evaluation has reached the maximum number of iterations, without converging.
	SortingOfClusterValues	Get/sets the sorting of cluster values after evaluation. It presumes that the generic type TDataSum implements the IComparable interface.

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object)
	Evaluate	Clusters the provided data. Please note that the data are not normalized. Thus, for multidimensional data, please normalize the data before!
	EvaluateClustersStandardDeviation	Evaluates for each cluster the standard deviation, i.e. the square root ( of the sum of squared distances divided by N-1)
	EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double)	Evaluates the Davies-Bouldin-Index. The exponent q (see EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double, Int32)) is set to 1, meaning that the mean Euclidean distance of the points to their respective centroid is used in the nominator.
	EvaluateDaviesBouldinIndex(FuncTDataSum, TDataSum, Double, Int32)	Evaluates the Davies-Bouldin-Index. The exponent q (used to calculate the average distance of the cluster points to their centroid) can be set as parameter.
	EvaluateMean2ndMomentOfDistances	Evaluates for each cluster the 2nd moment of the distances, i.e. the square root of the average of the squared Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
	EvaluateMeanDistances	Evaluates for each cluster the mean distance, i.e. the average of the Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
	EvaluateMeanNthMomentOfDistances	Evaluates for each cluster the mean distance, i.e. the square root ( of the sum of squared distances divided by N)
	EvaluateSumOfSquaredDistancesToClusterMean	Evaluates the sum of (squared distance of each point to its cluster center).
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object)
	GetFarthestPoint	Returns the index of the point that is farthest outside the mean of its cluster.
	GetFarthestPointOfBiggestCluster	Returns the index of the point that is farthest outside the mean of its cluster.
	GetHashCode	Serves as the default hash function. (Inherited from Object)
	GetType	Gets the Type of the current instance. (Inherited from Object)
	InitializeCentroidsAtRandom	Initialize the centroids with a randomly choosen value from the data set. Initializing in this way gives poor clustering results, thus we don't use it currently. Advantage: much faster than InitializeCentroidsUsingKMeansPlusPlus.
	InitializeCentroidsUsingKMeansPlusPlus	Initializes the cluster mean values using the KMeans++ procedure (http://en.wikipedia.org/wiki/K-means%2B%2B). This is slower than InitializeCentroidsAtRandom, but after initializing in this way, significant lesser iterations are neccessary.
	InternalEvaluateMeanNthMomentOfDistances	Evaluates for each cluster the 2nd moment of the distances, i.e. the square root of the average of the squared Euclidean distances (or whatever the distance function is) of the points to their respective centroid.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object)
	PatchEmptyClusters	Tries to fill up empty clusters, by searching the biggest cluster, and then use the farthest point from that biggest cluster to move to the empty cluster.
	ToString	Returns a string that represents the current object. (Inherited from Object)
	TryEvaluate	Clusters the provided data. Please note that the data are not normalized. Thus, for multidimensional data, please normalize the data before!
	UpdateClusterMeanValues	Calculates the mean values of the clusters from the data points.
	UpdateEachPointsMembership	Iterates over each data point, and determines to which cluster it belongs.

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Remarks

References: KMeans algorithm: https://en.wikipedia.org/wiki/K-means_clustering KMeans++ initialization: https://en.wikipedia.org/wiki/K-means%2B%2B

Reference

Altaxo.Calc.Clustering Namespace