private SilverlightEdge GetCentralEdge(List <SilverlightEdge> edges)
{
SilverlightEdge bestEdge = null;
double distance = double.MaxValue;
foreach (SilverlightEdge edge in edges)
{
double newDistance = edge.Position.CalculateDistance(CenterPoint);
if (newDistance < distance)
{
bestEdge = edge;
distance = newDistance;
}
}
return(bestEdge);
}
//∙Place K points into the space represented by the objects that are being clustered. These points represent
//initial group centroids.
//∙Assign each object to the group that has the closest centroid.
//∙When all objects have been assigned, recalculate the positions of the K centroids.
//∙Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into
//groups from which the metric to be minimized can be calculated.
//Table 1 and Fig. 2 show the results, after applying �-means clustering
public List <Cluster> CreateClusters(List <SilverlightEdge> edges, int nrOfClusters)
{
List <Cluster> returnCluster = new List <Cluster>();
CalculateBoundaries(edges);
CalculateRadius();
returnCluster = InitialClusters(edges, nrOfClusters);
while (RecalculateClusters(edges, ref returnCluster) == false)
{
}
SilverlightEdge centralEdge = GetCentralEdge(edges);
foreach (Cluster c in returnCluster)
{
if (!c.Edges.Contains(centralEdge))
{
c.Edges.Add(centralEdge);
}
}
return(returnCluster);
}
