KNIME Base Nodes version 4.0.2.v201909251340 by KNIME AG, Zurich, Switzerland
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.
To use this node in KNIME, install KNIME Core from the following update site:
You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.