KNIME Base Nodes version 4.3.0.v202012031743 by KNIME AG, Zurich, Switzerland
The fuzzy c-means algorithm is a well-known unsupervised learning
technique that can be used to reveal the underlying structure of the data.
Fuzzy clustering allows each data point to belong to several clusters, with
a degree of membership to each one.
Make sure that the input data is normalized to obtain better clustering
results.
The list of attributes to use can be set in the second tab of the
dialog.
The first output datatable provides the original datatable with the
cluster memberships to each cluster. The second datatable provides
the values of the cluster prototypes.
Additionally, it is possible to induce a noise cluster, to detect noise
in the dataset, based on the approach from R. N. Dave: 'Characterization
and detection of noise in clustering'.
To use this node in KNIME, install KNIME Base nodes from the following update site:
A zipped version of the software site can be downloaded here.
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