public double CalculateSilhouette(List <ClusterDistanceModel> clusteringResult)
{
Dictionary <int, double> lowestDissimilarities = new Dictionary <int, double>();
Dictionary <int, double> sameClusterDissimilarity = new Dictionary <int, double>();
for (int i = 0; i < NumberOfClusters; i++)
{
List <TransactionMatrixModel> clusterIPersons =
TransactionMatrix.Where(
m =>
clusteringResult.Where(n => n.CurrentCluster == i)
.Select(b => b.PersonId)
.Contains(m.PersonId)).ToList();
for (int j = 0; j < NumberOfClusters; j++)
{
List <TransactionMatrixModel> clusterJPersons =
TransactionMatrix.Where(
m =>
clusteringResult.Where(n => n.CurrentCluster == j)
.Select(b => b.PersonId)
.Contains(m.PersonId)).ToList();
foreach (TransactionMatrixModel personI in clusterIPersons)
{
double dissimilarity = CalculateDissimilarity(personI, clusterJPersons);
if (
clusteringResult.Where(m => m.CurrentCluster == j)
.Count(m => m.PersonId == personI.PersonId) == 0)
{
SaveDissimilarities(lowestDissimilarities, dissimilarity, personI.PersonId);
}
else
{
SaveDissimilarities(sameClusterDissimilarity, dissimilarity, personI.PersonId);
}
}
}
}
double resultSilhouette = 0;
foreach (KeyValuePair <int, double> lowestDissimilarity in lowestDissimilarities)
{
int personId = lowestDissimilarity.Key;
double silhouette = (lowestDissimilarities[personId] - sameClusterDissimilarity[personId]) /
Math.Max(lowestDissimilarities[personId], sameClusterDissimilarity[personId]);
resultSilhouette += silhouette;
}
return(resultSilhouette / lowestDissimilarities.Count);
}
private double[,] RecalculateCentroids(List <ClusterDistanceModel> results, double[,] prevCentroids)
{
double[,] centroids = new double[NumberOfClusters, 32];
//foreach person sum total values / total in cluster
for (int i = 0; i < NumberOfClusters; i++)
{
for (int j = 0; j < NumberOfVectors; j++)
{
List <int> persons = results.Where(m => m.CurrentCluster == i).Select(m => m.PersonId).ToList();
int count = TransactionMatrix.Count(m => persons.Contains(m.PersonId));
double sum = TransactionMatrix.Where(m => persons.Contains(m.PersonId)).Sum(m => m.Values[j]);
double mean = sum / count;
centroids[i, j] = count > 0 ? mean : prevCentroids[i, j];
}
}
return(centroids);
}
public Clustering(List <TransactionModel> transactions)
{
TransactionMatrix = TransformData(transactions);
NumberOfVectors = TransactionMatrix.Select(m => m.Values.Count()).First();
}
