Fuzzy c-Means (deprecated)

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.

Input ports

1. Type: Table
   Training data
   Datatable with training data. Make sure that the data are normalized!
2. Type: PMML
   PMML Preprocessing
   Optional PMML port object containing preprocessing operations.

Output ports

1. Type: Table
   Cluster Memberships
   Input table extended by cluster membership
2. Type: PMML
   Prototypes
   Cluster centers