Hierarchically clusters the input data.
Note: This node works only on small data sets. It keeps the entire data in memory and has cubic complexity.
There are two methods to do hierarchical clustering:
In order to determine the distance between clusters a measure has to be defined. Basically, there exist three methods to compare two clusters:
In order to measure the distance between two points a distance measure is necessary. You can choose between the Manhattan distance and the Euclidean distance, which corresponds to the L1 and the L2 norm.
The output is the same data as the input with one additional column with the clustername the data point is assigned to. Since a hierarchical clustering algorithm produces a series of cluster results, the number of clusters for the output has to be defined in the dialog.
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