Clusters data using hierarchical single-link algorithm.
|inPoints||Point2DArray||Array of points to cluster|
|inClusters||Integer*||2 - +||Number of clusters to extract|
|inMaxDistance||Real*||0.0 -||Maximum distance between two closest points in a cluster|
|outClusters||Point2DArrayArray||Resulting Point2D clusters|
- if inMaxDistance is set then every two points of mutual distance smaller or equal to inMaxDistance belong to one cluster;
- if inMaxDistance is not set then an array of sorted edges' lengths of minimum spanning tree of inPoints is determined and
a pair of two subsequent elements which makes a maximum difference is found.
Maximum distance is defined as the smaller element of such a pair.
In other words number of clusters is the number of vertical lines in the dendrogram cut by a horizontal line that can transverse the maximum distance vertically without intersecting a cluster.
This filter can throw an exception to report error. Read how to deal with errors in Error Handling.
List of possible exceptions:
|DomainError||Desired number of clusters is greater than inPoints size in ClusterPoints2D_SingleLink.|
|DomainError||Filter input inPoints is empty in ClusterPoints2D_SingleLink.|
|DomainError||Too big inPoints array in ClusterPoints2D_SingleLink.|
This filter is available on Expert Complexity Level.
- ClusterPoints2D – Clusters 2D points using K Means Clustering method.