private void _BuildDissimilarityMatrixParallel()
{
_dissimilarityMatrix = new DissimilarityMatrix();
Parallel.ForEach(_ClusterPairCollection(), clusterPair =>
{
double distanceBetweenTwoClusters = 0;
if (clusterPair.Cluster1.QuantityOfPatterns() == 1 && clusterPair.Cluster2.QuantityOfPatterns() == 1)
{
distanceBetweenTwoClusters = _distanceFunction(
clusterPair.Cluster1.GetPattern(0).GetAttributes(),
clusterPair.Cluster2.GetPattern(0).GetAttributes());
}
_dissimilarityMatrix.AddClusterPairAndDistance(clusterPair, distanceBetweenTwoClusters);
});
}
private double ComputeDistance(Cluster cluster1, Cluster cluster2, DissimilarityMatrix dissimilarityMatrix, ClusterDistanceStrategy strategy)
{
var distance1 = dissimilarityMatrix.ReturnClusterPairDistance(new ClusterPair(cluster1, cluster2.GetSubCluster(0)));
var distance2 = dissimilarityMatrix.ReturnClusterPairDistance(new ClusterPair(cluster1, cluster2.GetSubCluster(1)));
switch (strategy)
{
case ClusterDistanceStrategy.SingleLinkage: return(distance1 < distance2 ? distance1 : distance2);
case ClusterDistanceStrategy.CompleteLinkage: return(distance1 > distance2 ? distance1 : distance2);
case ClusterDistanceStrategy.AverageWeightedPairGroupMethodArithmeticMean: return((distance1 + distance2) / 2);
case ClusterDistanceStrategy.AverageUnweightedPairGroupMethodArithmeticMean:
return(((cluster2.GetSubCluster(0).TotalQuantityOfPatterns *distance1) / cluster2.TotalQuantityOfPatterns) +
((cluster2.GetSubCluster(1).TotalQuantityOfPatterns *distance2) / cluster2.TotalQuantityOfPatterns));
default: return(0);
}
}