KNIME Distance Matrix Extension version 4.3.0.v202011191420 by KNIME AG, Zurich, Switzerland
This node computes the Silhouette Coefficient for the provided clustering result. The Silhouette Coefficient is a useful
metric for evaluating clustering performance. For each row, it is computed using
(b - a) / max(a, b), where
is the mean intra-cluster distance and
b is the mean inter-cluster distance to the closest cluster. Additionally, a
second table containing the mean over all individual Silhouette Coefficients is calculated. The score can range from -1.0 to 1.0,
while the higher the score, the better. There have to be at least two clusters for the score to be computable.
By default, the Euclidean distance is used to calculate distances between rows. This may be changed by providing an optional distance function. If a distance function is supplied, the data column selection in the dialog will be ignored as the used columns are configured by the connected distance function.
Computing the Silhouette Coefficient is computationally expensive, thus it is recommended to subsample if the original dataset is large.
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.
You don't know what to do with this link? Read our NodePit Product and Node Installation Guide that explains you in detail how to install nodes to your KNIME Analytics Platform.
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.