private void CalculateClusterCenters()
{
for (int j = 0; j < this.Clusters.Count; j++)
{
ClusterCentroid c = this.Clusters[j];
double uX = 0.0;
double uY = 0.0;
double l = 0.0;
for (int i = 0; i < this.Points.Count; i++)
{
ClusterPoint p = this.Points[i];
double uu = Math.Pow(U[i, j], this.Fuzzyness);
uX += uu * c.X;
uY += uu * c.Y;
l += uu;
}
c.X = ((int)(uX / l));
c.Y = ((int)(uY / l));
this.Log += string.Format("Cluster Centroid: ({0}; {1})" + System.Environment.NewLine, c.X, c.Y);
}
}
public CMeansAlgorithm(List <ClusterPoint> points, List <ClusterCentroid> clusters, float fuzzy)
{
if (points == null)
{
throw new ArgumentNullException("points");
}
if (clusters == null)
{
throw new ArgumentNullException("clusters");
}
this.Points = points;
this.Clusters = clusters;
U = new double[this.Points.Count, this.Clusters.Count];
//Uk = new double[this.Points.Count, this.Clusters.Count];
this.Fuzzyness = fuzzy;
double diff;
// Iterate through all points to create initial U matrix
for (int i = 0; i < this.Points.Count; i++)
{
ClusterPoint p = this.Points[i];
double sum = 0.0;
for (int j = 0; j < this.Clusters.Count; j++)
{
ClusterCentroid c = this.Clusters[j];
diff = Math.Sqrt(Math.Pow(p.X - c.X, 2.0) + Math.Pow(p.Y - c.Y, 2.0));
U[i, j] = (diff == 0) ? Eps : diff;
sum += U[i, j];
}
double sum2 = 0.0;
for (int j = 0; j < this.Clusters.Count; j++)
{
U[i, j] = 1.0 / Math.Pow(U[i, j] / sum, 2.0 / (Fuzzyness - 1.0));
sum2 += U[i, j];
}
for (int j = 0; j < this.Clusters.Count; j++)
{
U[i, j] = U[i, j] / sum2;
}
}
this.RecalculateClusterIndexes();
}
private double CalculateEulerDistance(ClusterPoint p, ClusterCentroid c)
{
return(Math.Sqrt(Math.Pow(p.X - c.X, 2) + Math.Pow(p.Y - c.Y, 2)));
}
private void ExecAlg()
{
progressBar1.Value = 0;
Point pt = new Point();
if (dgvGiven.Rows.Count - 1 > 0)
{
txtResult.Clear();
List <PointInfo> points = new List <PointInfo>();
for (int i = 0; i < dgvGiven.Rows.Count - 1; i++)
{
pt.X = Convert.ToInt32(dgvGiven.Rows[i].Cells[0].Value);
pt.Y = Convert.ToInt32(dgvGiven.Rows[i].Cells[1].Value);
points.Add(new PointInfo(pt.X, pt.Y));
}
if (rbAlgDBSCAN.Checked)
{
double eps = (double)nudEps.Value;
int minPts = (int)nudMinPts.Value;
List <List <PointInfo> > clusters = DBSCANClass.GetClusters(points, eps, minPts);
ShowResultLog(clusters, points);
}
else if (rbAlgKMean.Checked)
{
int K = (int)nudNumberOfClusters.Value;
int PointsCount = dgvGiven.Rows.Count - 1;
double[][] rawData = new double[PointsCount][];
for (int i = 0; i < PointsCount; i++)
{
rawData[i] = new double[] { Convert.ToDouble(dgvGiven.Rows[i].Cells[0].Value), Convert.ToDouble(dgvGiven.Rows[i].Cells[1].Value) };
}
List <List <PointInfo> > clusters = K_Mean.GetClusters(rawData, K);
//List<PointInfo> results = KMeanPoints.OrderBy(x => x.ClusterId).ToList();
ShowResultLog(clusters, points);
}
else if (rbAlgFCM.Checked)
{
int CentroidCount = 0;
List <ClusterPoint> clusterPoints = new List <ClusterPoint>();
List <ClusterCentroid> centroids = new List <ClusterCentroid>();
for (int i = 0; i < dgvGiven.Rows.Count - 1; i++)
{
double x = Convert.ToDouble(dgvGiven.Rows[i].Cells[0].Value);
double y = Convert.ToDouble(dgvGiven.Rows[i].Cells[1].Value);
clusterPoints.Add(new ClusterPoint(x, y));
if ((bool)dgvGiven.Rows[i].Cells[3].Value == true)
{
CentroidCount++;
centroids.Add(new ClusterCentroid(x, y));
}
}
if (CentroidCount != (int)nudNumberOfClusters.Value)
{
MessageBox.Show("The number of Centroids (Clusters) is not equal to number of selected centroid points\r\nPlease choose centroid points equal to number of clusters count", "Error on centroid points selection", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
else
{
CMeansAlgorithm alg = new CMeansAlgorithm(clusterPoints, centroids, (float)nudFuzzyExp.Value);
int iterations = alg.Run(Math.Pow(10, -5), (int)nudIterations.Value);
double[,] Matrix = alg.U;
txtResult.Clear();
txtResult.Text = string.Format("FCM Algorithm Results with ({0}):\r\n", iterations);
for (int j = 0; j < clusterPoints.Count; j++)
{
for (int i = 0; i < centroids.Count; i++)
{
ClusterPoint p = clusterPoints[j];
txtResult.Text += string.Format("\r\n{0:00} Point: ({1};{2}) ClusterIndex: {3} Value: {4:0.000}", j + 1, p.X, p.Y, p.ClusterIndex, Matrix[j, i]);
pt.X = (int)p.X;
pt.Y = (int)p.Y;
PutPoint(pt, getColor((int)p.ClusterIndex), tbZoom.Value + 1);
}
}
txtResult.Text += string.Format("\r\nIteration count: {0}", iterations);
}
}
}
else
{
MessageBox.Show("There are no ay information to calculate DBSCAN", "No data exist to mining", MessageBoxButtons.OK, MessageBoxIcon.Warning);
}
}