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BalancedKMeans Class

Balanced K-Means algorithm. Note: The balanced clusters will be available in the Labels property of this instance!
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningKMeans
      Accord.MachineLearningBalancedKMeans

Namespace:  Accord.MachineLearning
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.7.0
Syntax
[SerializableAttribute]
public class BalancedKMeans : KMeans
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The BalancedKMeans type exposes the following members.

Constructors
Properties
  NameDescription
Public propertyCentroids
Gets or sets the cluster centroids.
(Inherited from KMeans.)
Public propertyClusters
Gets the clusters found by K-means.
(Inherited from KMeans.)
Public propertyComputeCovariances
Gets or sets whether covariance matrices for the clusters should be computed at the end of an iteration. Default is true.
(Inherited from KMeans.)
Public propertyComputeError
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.
(Inherited from KMeans.)
Public propertyDimension
Gets the dimensionality of the data space.
(Inherited from KMeans.)
Public propertyDistance
Gets or sets the distance function used as a distance metric between data points.
(Inherited from KMeans.)
Public propertyError
Gets the cluster distortion error after the last call to this class' Compute methods.
(Inherited from KMeans.)
Public propertyIterations
Gets the number of iterations performed in the last call to this class' Compute methods.
(Inherited from KMeans.)
Public propertyK
Gets the number of clusters.
(Inherited from KMeans.)
Public propertyLabels
Public propertyMaxIterations
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.
(Inherited from KMeans.)
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)
Public propertyToken
Gets or sets a cancellation token that can be used to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)
Public propertyTolerance
Gets or sets the relative convergence threshold for stopping the algorithm. Default is 1e-5.
(Inherited from KMeans.)
Public propertyUseSeeding
Gets or sets the strategy used to initialize the centroids of the clustering algorithm. Default is KMeansPlusPlus.
(Inherited from KMeans.)
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Methods
  NameDescription
Public methodCompute(Double) Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)
Public methodCompute(Double, Double) Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)
Public methodCompute(Double, Double) Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)
Protected methodComputeInformation(Double)
Computes the information about each cluster (covariance, proportions and error).
(Inherited from KMeans.)
Protected methodComputeInformation(Double, Int32)
Computes the information about each cluster (covariance, proportions and error).
(Inherited from KMeans.)
Protected methodconverged
Determines if the algorithm has converged by comparing the centroids between two consecutive iterations.
(Inherited from KMeans.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.
(Inherited from Object.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Public methodLearn
Learns a model that can map the given inputs to the desired outputs. Note: the model created by this function will not be able to produce balanced clusterings. To retrieve the balanced labels, check the Labels property of this class after calling this function.
(Overrides KMeansLearn(Double, Double).)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRandomize
Randomizes the clusters inside a dataset.
(Inherited from KMeans.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
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Extension Methods
  NameDescription
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodToT
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
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Remarks

The Balanced k-Means algorithm attempts to find a clustering where each cluster has approximately the same number of data points. The Balanced k-Means implementation used in the framework uses the Munkres algorithm to solve the assignment problem thus enforcing balance between the clusters.

Note: the Learn(Double, Double) method of this class will return the centroids of balanced clusters, but please note that these centroids cannot be used to obtain balanced clusterings for another (or even the same) data set. Instead, in order to inspect the balanced clustering that has been obtained after calling Learn(Double, Double), please take a look at the contents of the Labels property.

Examples
How to perform clustering with Balanced K-Means.
Accord.Math.Random.Generator.Seed = 0;

// Declare some observations
double[][] observations =
{
    new double[] { -5, -2, -1 },
    new double[] { -5, -5, -6 },
    new double[] {  2,  1,  1 },
    new double[] {  1,  1,  2 },
    new double[] {  1,  2,  2 },
    new double[] {  3,  1,  2 },
    new double[] { 11,  5,  4 },
    new double[] { 15,  5,  6 },
    new double[] { 10,  5,  6 },
};

double[][] orig = observations.MemberwiseClone();

// Create a new K-Means algorithm with 3 clusters 
BalancedKMeans kmeans = new BalancedKMeans(3)
{
    // Note: in balanced k-means the chances of the algorithm oscillating
    // between two solutions increases considerably. For this reason, we 
    // set a max-iterations limit to avoid iterating indefinitely.
    MaxIterations = 100
};

// Compute the algorithm, retrieving an integer array
//  containing the labels for each of the observations
KMeansClusterCollection clusters = kmeans.Learn(observations);

// As a result, the first two observations should belong to the
//  same cluster (thus having the same label). The same should
//  happen to the next four observations and to the last three.
int[] labels = clusters.Decide(observations);
See Also