KModesT Class

[SerializableAttribute]
public class KModes<T> : ParallelLearningBase, IUnsupervisedLearning<KModesClusterCollection<T>, T[], int>, 
	IClusteringAlgorithm<T[]>, IUnsupervisedLearning<IClusterCollection<T[]>, T[], int>

<SerializableAttribute>
Public Class KModes(Of T)
	Inherits ParallelLearningBase
	Implements IUnsupervisedLearning(Of KModesClusterCollection(Of T), T(), Integer), 
	IClusteringAlgorithm(Of T()), IUnsupervisedLearning(Of IClusterCollection(Of T()), T(), Integer)

Accord.Math.Random.Generator.Seed = 0;

// Declare some observations
byte[][] observations = new[]
{
    new byte[] { 0, 0   }, // a
    new byte[] { 0, 1   }, // a
    new byte[] { 0, 1   }, // a
    new byte[] { 1, 1   }, // a

    new byte[] { 5, 3   }, // b
    new byte[] { 6, 8   }, // b
    new byte[] { 6, 8   }, // b
    new byte[] { 6, 7   }, // b
    new byte[] { 5, 8   }, // b

    new byte[] { 12, 14 }, // c
    new byte[] { 12, 14 }, // c
    new byte[] { 13, 14 }, // c
};

// Create a new 3-Modes algorithm using the Hamming distance
var kmodes = new KModes<byte>(k: 3, distance: new Hamming())
{
    MaxIterations = 100
};

// Compute and retrieve the data centroids
var clusters = kmodes.Learn(observations);

// Use the centroids to parition all the data
int[] labels = clusters.Decide(observations);