/// <summary>
/// Visit every neighbour point to find if there are any neighbours to the neighbours of the starting point
/// </summary>
/// <param name="allPoints">The given data for clustering</param>
/// <param name="point">The point you want to check the neighbours of</param>
/// <param name="neighborPts">The neighbours of the given point</param>
/// <param name="clusterId">Current Id for a cluster</param>
/// <param name="epsilon">The max amount of range around a point to still be considered to be part of a cluster (be a neighbour)</param>
/// <param name="minPts">The minimum amount of points a set of points should have to be marked as a cluster</param>
private static void ExpandCluster(DbscanPoint[] allPoints, DbscanPoint point, DbscanPoint[] neighborPts, int clusterId, double epsilon, int minPts)
{
//Visit each neighbours point if it hasn't been visited yet
point.ClusterId = clusterId;
for (int i = 0; i < neighborPts.Length; i++)
{
DbscanPoint pn = neighborPts[i];
if (!pn.IsVisited)
{
pn.IsVisited = true;
//Retrieve all neighbour points to this given point
var neighborPts2 = RegionQuery(allPoints, pn, epsilon);
//If the found points are more than the min amount of points, union it with the already selected points
if (neighborPts2.Length >= minPts)
{
neighborPts = neighborPts.Union(neighborPts2).ToArray();
}
}
//Assign the current clusterId to the point
if (pn.ClusterId == 0)
{
pn.ClusterId = clusterId;
}
}
}
/// <summary>
/// Calculates the euclidean distance between the Core Point for every given point
/// </summary>
/// <param name="allPoints">All points you want to know the distance of between the corePoint</param>
/// <param name="corePoint">The point you are using to calculate the distances to</param>
/// <param name="epsilon">The max amount of range around a point to still be considered to be part of a cluster (be a neighbour)</param>
/// <returns></returns>
private static DbscanPoint[] RegionQuery(DbscanPoint[] allPoints, DbscanPoint corePoint, double epsilon)
{
//If the length of Dimensions are not equal to eachother, return an empty array
if (allPoints.Count(x => x.Coordinates.Count() != corePoint.Coordinates.Length) != 0)
{
return(new DbscanPoint[0]);
}
//Return all points where the euclidean distance is within the epsilon range
return(allPoints.Where(point => SimilarityCalculations.CalculateEuclideanDistance(point.Coordinates, corePoint.Coordinates) < epsilon).ToArray());
}
/// <summary>
/// Computes every point in the data to dertermine which cluster it belongs to and if it is and outlier
/// </summary>
/// <param name="points">The data you want to cluster (double array per point containing all dimensions of that point)</param>
/// <param name="epsilon">The max amount of range around a point to still be considered to be part of a cluster (be a neighbour)</param>
/// <param name="minPts">The minimum amount of points a set of points should have to be marked as a cluster</param>
/// <returns></returns>
public static DbscanPoint[] ComputeCluster(double[][] points, double epsilon, int minPts)
{
//Generate DbScanPoints from the given data
var allPoints = new DbscanPoint[points.Length];
for (int i = 0; i < allPoints.Length; i++)
{
allPoints[i] = new DbscanPoint(points[i]);
}
int clusterId = 0;
//Visit every point in the data
foreach (DbscanPoint p in allPoints)
{
if (p.IsVisited)
{
continue;
}
p.IsVisited = true;
//Execute the region query to retrieve all neighbours points to the given point
DbscanPoint[] neighborPts = RegionQuery(allPoints, p, epsilon);
//If the amount of neighbours from the query is less than the defined min amount of point, mark them as outliers
if (neighborPts.Length < minPts)
{
p.ClusterId = -1;
}
//Else visit every neighbour point that is in range of the epsilon
else
{
clusterId++;
ExpandCluster(allPoints, p, neighborPts, clusterId, epsilon, minPts);
}
}
return(allPoints);
}
