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KModes Class

k-Modes algorithm.
Inheritance Hierarchy
SystemObject
  Accord.MachineLearningParallelLearningBase
    Accord.MachineLearningKModesInt32
      Accord.MachineLearningKModes

Namespace:  Accord.MachineLearning
Assembly:  Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.5.0
Syntax
[SerializableAttribute]
public class KModes : KModes<int>
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The KModes type exposes the following members.

Constructors
  NameDescription
Public methodKModes
Initializes a new instance of K-Modes algorithm
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Properties
  NameDescription
Public propertyClusters
Gets the clusters found by K-modes.
(Inherited from KModesT.)
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 KModesT.)
Public propertyDimension
Gets the dimensionality of the data space.
(Inherited from KModesT.)
Public propertyDistance
Gets or sets the distance function used as a distance metric between data points.
(Inherited from KModesT.)
Public propertyError
Gets the cluster distortion error (the average distance between data points and the cluster centroids) after the last call to this class' Compute methods.
(Inherited from KModesT.)
Public propertyInitialization
Gets or sets the strategy used to initialize the centroids of the clustering algorithm. Default is KMeansPlusPlus.
(Inherited from KModesT.)
Public propertyIterations
Gets the number of iterations performed in the last call to this class' Compute methods.
(Inherited from KModesT.)
Public propertyK
Gets the number of clusters.
(Inherited from KModesT.)
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 KModesT.)
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 KModesT.)
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Methods
  NameDescription
Public methodCompute Obsolete.
Divides the input data into K clusters.
(Inherited from KModesT.)
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.
(Inherited from KModesT.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
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 MethodToTOverloaded.
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.)
Public Extension MethodToTOverloaded.
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 Matrix.)
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Remarks

The k-Modes algorithm is a variant of the k-Means which instead of locating means attempts to locate the modes of a set of points. As the algorithm does not require explicit numeric manipulation of the input points (such as addition and division to compute the means), the algorithm can be used with arbitrary (generic) data structures.

This is the specialized, non-generic version of the K-Modes algorithm that is set to work on Int32 arrays.

See Also