The following node is available in the Open Source KNIME predictive analytics and data mining platform version 2.7.1. Discover over 1000 other nodes, as well as enterprise functionality at http://knime.com.

Hierarchical Clustering (DistMatrix)

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:

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:

The distance information used by this node is read from an distance vector column that must be available in the input data. You can always calculate the distance matrix using the corresponding Calculate node.

Dialog Options

Distance matrix column
If the selected row distance is Distance Matrix then you must choose the column containing the distance matrix here. For other distance function the node automatically takes all compatible input columns and computed the distances between rows on-the-fly.
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.

Ports

Input Ports
0 The data that should be clustered using hierarchical clustering.
Output Ports
0 The hierarchical cluster tree that can be fed into the Hierarchical Cluster View node or the Hierarchical Cluster Assigner node.
This node is contained in KNIME Distance Matrix Extension provided by KNIME GmbH, Konstanz, Germany.