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Hierarchical Clustering (DistMatrix)

KNIME Distance Matrix Extension version 4.3.0.v202011191420 by KNIME AG, Zurich, Switzerland

Hierarchically clusters the input data using a distance matrix.
Note: This node works only on small data sets, because it has cubic complexity.
There are two methods to do hierarchical clustering:

  • Top-down or divisive, i.e. the algorithm starts with all data points in one huge cluster and the most dissimilar datapoints are divided into subclusters until each cluster consists of exactly one data point.
  • Bottom-up or agglomerative, i.e. the algorithm starts with every datapoint as one single cluster and tries to combine the most similar ones into superclusters until it ends up in one huge cluster containing all subclusters.
This algorithm works agglomerative.

In order to determine the distance between clusters a measure has to be defined. Basically, there exist three methods to compare two clusters:

  • Single Linkage: defines the distance between two clusters c1 and c2 as the minimal distance between any two points x, y with x in c1 and y in c2.
  • Complete Linkage: defines the distance between two clusters c1 and c2 as the maximal distance between any two points x, y with x in c1 and y in c2.
  • Average Linkage: defines the distance between two clusters c1 and c2 as the mean distance between all points in c1 and c2.

The distance information used by this node is either read from a distance vector column that must be available in the input data or is computed directly with usage of a connected distance measure. You can always calculate the distance matrix using the corresponding calculate node.


Distance matrix column
Select the column containing the distance values. This option is disabled if a distance measure is connected (Port 1).
Linkage type
Which method to use to measure the distance between points (as described above)
Ignore missing values
By default, the node ignores rows with missing values completely. If instead an error should be reported, disable this option.

Input Ports

Contains the data that should be clustered using hierarchical clustering and the the optional distance matrix.
Optional distance measure, which renders the distance matrix at Port 0 unnecessary.

Output Ports

The hierarchical cluster tree that can be fed into the Hierarchical Cluster View node or the Hierarchical Cluster Assigner node.

Best Friends (Incoming)

Best Friends (Outgoing)



To use this node in KNIME, install KNIME Distance Matrix from the following update site:


A zipped version of the software site can be downloaded here.

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