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.
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'.
If the optional PMML inport is connected and contains
preprocessing operations in the TransformationDictionary those are
added to the learned model.
0 | Datatable with training data. Make sure that the data are normalized! |
1 | Optional PMML port object containing preprocessing operations. |
0 | Input table extended by cluster membership |
1 | Cluster centers |