/// <summary>
/// Seperates a dataset into clusters or groups with similar characteristics
/// </summary>
/// <param name="clusterCount">The number of clusters or groups to form</param>
/// <param name="data">An array containing data that will be clustered</param>
/// <returns>A collection of clusters of data</returns>
public static ClusterCollection ClusterDataSet(int clusterCount, double[,] data, string type)
{
//bool stableClusterFormation = false;
int clusterNumber = 0;
int rowCount = data.GetUpperBound(0) + 1;
int fieldCount = data.GetUpperBound(1) + 1;
int stableClustersCount = 0;
int iterationCount = 0;
double[] dataPoint;
Random random = new Random();
Cluster cluster = null;
ClusterCollection clusters = new ClusterCollection();
System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);
while (clusterNumbers.Count < clusterCount)
{
clusterNumber = random.Next(0, rowCount - 1);
if (!clusterNumbers.Contains(clusterNumber))
{
cluster = new Cluster();
clusterNumbers.Add(clusterNumber);
dataPoint = new double[fieldCount];
for (int field = 0; field < fieldCount; field++)
{
dataPoint.SetValue((data[clusterNumber, field]), field);
}
cluster.Add(dataPoint);
clusters.Add(cluster);
}
}
int compteur = 0;
while (stableClustersCount != clusters.Count)
{
stableClustersCount = 0;
bool isNotGood = false;
ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data, type);
for (int tempo = 0; tempo < newClusters.Count; tempo++)
{
if (newClusters[tempo].Count < 1)
{
isNotGood = true;
}
}
if (compteur == 10)
{
return(clusters);
}
if (isNotGood)
{
compteur++;
continue;
}
for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
{
double distance = 0;
switch (type)
{
case "DTW":
distance = (KMeans.DtwDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
case "Manhattan":
distance = (KMeans.ManhattanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
default:
distance = (KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
}
if (distance == 0)
{
stableClustersCount++;
}
}
iterationCount++;
clusters = newClusters;
}
return(clusters);
}
/// <summary>
/// Seperates a dataset into clusters or groups with similar characteristics
/// </summary>
/// <param name="clusterCount">The number of clusters or groups to form</param>
/// <param name="data">An array containing data that will be clustered</param>
/// <returns>A collection of clusters of data</returns>
public static ClusterCollection ClusterDataSet(int clusterCount, double [,] data)
{
//bool stableClusterFormation = false;
int clusterNumber = 0;
int rowCount = data.GetUpperBound(0) + 1;
int fieldCount = data.GetUpperBound(1) + 1;
int stableClustersCount = 0;
int iterationCount = 0;
double [] dataPoint;
Random random = new Random();
Cluster cluster = null;
ClusterCollection clusters = new ClusterCollection();
System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);
while (clusterNumbers.Count < clusterCount)
{
clusterNumber = random.Next(0, rowCount - 1);
if (!clusterNumbers.Contains(clusterNumber))
{
cluster = new Cluster();
clusterNumbers.Add(clusterNumber);
dataPoint = new double[fieldCount];
for (int field = 0; field < fieldCount; field++)
{
dataPoint.SetValue((data[clusterNumber, field]), field);
}
cluster.Add(dataPoint);
clusters.Add(cluster);
}
}
while (stableClustersCount != clusters.Count)
{
stableClustersCount = 0;
ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data);
for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
{
if ((KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean)) == 0)
{
stableClustersCount++;
}
}
iterationCount++;
clusters = newClusters;
}
return(clusters);
}
/// <summary>
/// Seperates a dataset into clusters or groups with similar characteristics
/// </summary>
/// <param name="clusterCount">The number of clusters or groups to form</param>
/// <param name="data">An array containing data that will be clustered</param>
/// <returns>A collection of clusters of data</returns>
public static ClusterCollection ClusterDataSet(int clusterCount, double[,] data, string type)
{
//bool stableClusterFormation = false;
int clusterNumber = 0;
int rowCount = data.GetUpperBound(0) + 1;
int fieldCount = data.GetUpperBound(1) + 1;
int stableClustersCount = 0;
int iterationCount = 0;
double[] dataPoint;
Random random = new Random();
Cluster cluster = null;
ClusterCollection clusters = new ClusterCollection();
System.Collections.ArrayList clusterNumbers = new System.Collections.ArrayList(clusterCount);
while (clusterNumbers.Count < clusterCount)
{
clusterNumber = random.Next(0, rowCount - 1);
if (!clusterNumbers.Contains(clusterNumber))
{
cluster = new Cluster();
clusterNumbers.Add(clusterNumber);
dataPoint = new double[fieldCount];
for (int field = 0; field < fieldCount; field++)
{
dataPoint.SetValue((data[clusterNumber, field]), field);
}
cluster.Add(dataPoint);
clusters.Add(cluster);
}
}
int compteur = 0;
while (stableClustersCount != clusters.Count)
{
stableClustersCount = 0;
bool isNotGood = false;
ClusterCollection newClusters = KMeans.ClusterDataSet(clusters, data, type);
for (int tempo = 0; tempo < newClusters.Count; tempo++)
if (newClusters[tempo].Count < 1)
isNotGood = true;
if (compteur == 10)
return clusters;
if (isNotGood)
{
compteur++;
continue;
}
for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
{
double distance = 0;
switch (type)
{
case "DTW":
distance = (KMeans.DtwDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
case "Manhattan":
distance = (KMeans.ManhattanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
default:
distance = (KMeans.EuclideanDistance(newClusters[clusterIndex].ClusterMean, clusters[clusterIndex].ClusterMean));
break;
}
if (distance == 0)
{
stableClustersCount++;
}
}
iterationCount++;
clusters = newClusters;
}
return clusters;
}
