| public Add ( double data ) : void | ||
| data | double | A 1-dimensional array containing data that will be added to the cluster |
| return | void | |
public ClusterCollection RandomSeeding(int k, double[][] data)
{
int size = data.Length;
double[][] seeds = new double[k][];
Random random = new Random();
Hashtable random_table = new Hashtable();
Cluster cluster = null;
ClusterCollection init_clusters = new ClusterCollection();
for (int i = 0; i < k;)
{
int r = random.Next(size - 1);
if (!random_table.ContainsKey(r))
{
random_table.Add(r, 0);
seeds[i] = new double[3];
seeds[i][0] = data[r][0]; seeds[i][1] = data[r][1]; seeds[i][2] = data[r][2];
cluster = new Cluster();
cluster.Add(seeds[i]);
init_clusters.Add(cluster);
i++;
}
}
return(init_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 ClusterCollection ClusterDataSet(int clusterCount, double [][] data)
{
int rowCount = data.Length;
int stableClustersCount = 0;
Cluster cluster = null;
ClusterCollection clusters = new ClusterCollection();
//setup seed clusters
for (int i = 0; i < clusterCount; i++)
{
cluster = new Cluster();
cluster.Add(data[i]);
clusters.Add(cluster);
}
//DateTime start = DateTime.Now;
//Console.WriteLine("Start clustering {0} objects into {1} clusters: {2}", rowCount.ToString(), clusterCount.ToString(), start.ToLongTimeString());
//do actual clustering
int iterationCount = 0;
while (stableClustersCount != clusters.Count)
{
iterationCount++;
stableClustersCount = 0;
//Do actual clustering
//Console.WriteLine("Start Cluster for ineration {0}: {1}", iterationCount, DateTime.Now.ToLongTimeString());
ClusterCollection newClusters = this.ClusterDataSet(clusters, data);
//Console.WriteLine(" End Cluster for ineration {0}: {1}", iterationCount, DateTime.Now.ToLongTimeString());
for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
{
double[] originalClusterMean = clusters[clusterIndex].ClusterMean;
double[] newClusterMean = newClusters[clusterIndex].ClusterMean;
double distance = this.EuclideanDistance(newClusterMean, originalClusterMean);
if (distance == 0)
{
stableClustersCount++;
//Console.WriteLine("{0} stable clusters out of {1}", stableClustersCount.ToString(), clusterCount.ToString());
}
}
clusters = newClusters;
}
//DateTime end = DateTime.Now;
//TimeSpan span = end - start;
//Console.WriteLine("End clustering {0} objects into {1} clusters with {2} iterations: {3}", rowCount.ToString(), clusterCount.ToString(), iterationCount, end.ToLongTimeString());
//Console.WriteLine("Clustering {0} objects into {1} clusters took {2} seconds", rowCount.ToString(), clusterCount.ToString(), span.TotalSeconds);
//Console.WriteLine();
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 ClusterCollection ClusterDataSet(int clusterCount, double [][] data)
{
int rowCount = data.Length;
int stableClustersCount = 0;
Cluster cluster = null;
ClusterCollection clusters = new ClusterCollection();
//setup seed clusters
for (int i = 0; i < clusterCount; i++)
{
cluster = new Cluster();
cluster.Add(data[i]);
clusters.Add(cluster);
}
DateTime start = DateTime.Now;
Console.WriteLine("Start clustering {0} objects into {1} clusters: {2}", rowCount.ToString(), clusterCount.ToString(), start.ToLongTimeString());
//do actual clustering
int iterationCount = 0;
while (stableClustersCount != clusters.Count)
{
iterationCount++;
stableClustersCount = 0;
//Do actual clustering
//Console.WriteLine("Start Cluster for ineration {0}: {1}", iterationCount, DateTime.Now.ToLongTimeString());
ClusterCollection newClusters = this.ClusterDataSet(clusters, data);
//Console.WriteLine(" End Cluster for ineration {0}: {1}", iterationCount, DateTime.Now.ToLongTimeString());
for (int clusterIndex = 0; clusterIndex < clusters.Count; clusterIndex++)
{
double[] originalClusterMean = clusters[clusterIndex].ClusterMean;
double[] newClusterMean = newClusters[clusterIndex].ClusterMean;
double distance = this.EuclideanDistance(newClusterMean, originalClusterMean);
if (distance ==0)
{
stableClustersCount++;
//Console.WriteLine("{0} stable clusters out of {1}", stableClustersCount.ToString(), clusterCount.ToString());
}
}
clusters = newClusters;
}
DateTime end = DateTime.Now;
TimeSpan span = end - start;
Console.WriteLine("End clustering {0} objects into {1} clusters with {2} iterations: {3}", rowCount.ToString(), clusterCount.ToString(), iterationCount, end.ToLongTimeString());
Console.WriteLine("Clustering {0} objects into {1} clusters took {2} seconds", rowCount.ToString(), clusterCount.ToString(), span.TotalSeconds);
Console.WriteLine();
return clusters;
}
public ClusterCollection RandomSeeding(int k, double[][] data)
{
int size = data.Length;
double[][] seeds = new double[k][];
Random random = new Random();
Hashtable random_table = new Hashtable();
Cluster cluster = null;
ClusterCollection init_clusters = new ClusterCollection();
for (int i = 0; i < k; )
{
int r = random.Next(size - 1);
if(!random_table.ContainsKey(r))
{
random_table.Add(r,0);
seeds[i] = new double[3];
seeds[i][0]=data[r][0];seeds[i][1]=data[r][1];seeds[i][2]=data[r][2];
cluster = new Cluster();
cluster.Add(seeds[i]);
init_clusters.Add(cluster);
i++;
}
}
return init_clusters;
}
