BinarySplit Class

[SerializableAttribute]
public class BinarySplit : KMeans

<SerializableAttribute>
Public Class BinarySplit
	Inherits KMeans

// Use a fixed seed for reproducibility
Accord.Math.Random.Generator.Seed = 0;

// Declare some data to be clustered
double[][] input =
{
    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 },
};

// Create a new binary split with 3 clusters 
BinarySplit binarySplit = new BinarySplit(3);

// Learn a data partitioning using the Binary Split algorithm
KMeansClusterCollection clustering = binarySplit.Learn(input);

// Predict group labels for each point
int[] output = clustering.Decide(input);

// 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.