public int[] ClusterKMeans(float[][] Data,
ClusterNumberIdentificationType type, int DefaultK, int MaxK)
{
int bestK = IdentifyOptimalClusterNumber(Data, type, DefaultK, MaxK);
//Debug.Log("Best # of Clusters is " + bestK);
float[][] InitCentroids = KMeansInitKZZ(Data, bestK);
int[] KClass = KMeansClassification(Data, InitCentroids, 2);
return(KClass);
}
public int IdentifyOptimalClusterNumber(float[][] Data,
ClusterNumberIdentificationType type, int DefaultK, int MaxK)
{
int bestK = 0;
switch (type)
{
case ClusterNumberIdentificationType.None:
return(DefaultK);
// 0. Find K from N-based heuristic
case ClusterNumberIdentificationType.NBased:
{
return(ClusterNumberNBased(Data));
}
// 1. Find K from elbow in Wk "Scree" Plot
case ClusterNumberIdentificationType.ScreeElbow:
{
return(ClusterNumberScreeElbow(Data, MaxK));
}
// 2. Find K from maximum Silhouette
case ClusterNumberIdentificationType.Silhouette:
{
return(ClusterNumberSilhouette(Data, MaxK, 2));
}
// 3. Find K from Gap between actual and simulated
case ClusterNumberIdentificationType.Gap:
{
return(ClusterNumberGap(Data, MaxK));
}
// 4. Find K from cluster compactness
case ClusterNumberIdentificationType.KizanowskiLai:
{
return(ClusterNumberKLDiff(Data, MaxK));
}
// 5. Find K from change in Wk
case ClusterNumberIdentificationType.Hartigan:
{
return(ClusterNumberHartigan(Data, MaxK));
}
// 6. Find K from change in ratio between Wk and Bk
case ClusterNumberIdentificationType.VRC:
{
return(OptimalKfromVRC(Data, MaxK));
}
// 7. All of them! -> Min
case ClusterNumberIdentificationType.Omnibus:
{
List <int> OptimalKList = new List <int>();
OptimalKList.Add(ClusterNumberNBased(Data));
OptimalKList.Add(ClusterNumberScreeElbow(Data, MaxK));
OptimalKList.Add(ClusterNumberSilhouette(Data, MaxK, 2));
OptimalKList.Add(ClusterNumberGap(Data, MaxK));
OptimalKList.Add(ClusterNumberKLDiff(Data, MaxK));
OptimalKList.Add(ClusterNumberHartigan(Data, MaxK));
OptimalKList.Add(OptimalKfromVRC(Data, MaxK));
return(OptimalKList.Min());
}
}
return(bestK);
}
