k-Means
This node outputs the cluster centers for a predefined number of
clusters (no dynamic number of clusters).
K-means performs a crisp clustering that assigns a data
vector to exactly one cluster. The algorithm terminates when the
cluster assignments do not change anymore.
The clustering algorithm uses the Euclidean distance on the selected
attributes. The data is not normalized by the node (if required,
you should consider to use the "Normalizer" as a preprocessing step).
If the optional PMML inport is connected and contains
preprocessing operations in the TransformationDictionary those are
added to the learned model.
The node can be configured as follows:
Dialog Options
- number of clusters
-
The number of clusters (cluster centers) to be created.
- max number of iterations
-
The number of iterations after which the algorithm terminates,
independent of the accuracy improvement of the cluster centers.
Ports
Input Ports
0 |
Input to clustering. All
numerical values and only these are considered for clustering. |
1 |
Optional PMML port object
containing preprocessing operations. |
Output Ports
0 |
The input data labeled with the
cluster they are contained in. |
1 |
PMML cluster model |
Views
- Cluster View
-
Displays the cluster prototypes in a tree-like structure, with each
node containing the coordinates of the cluster center.
This node is contained in KNIME Base Nodes
provided by KNIME GmbH, Konstanz, Germany.