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:
Options
Popular Successors
- Entropy Scorer6 %
CSV Writer3 %- X-Aggregator3 %
- X-Partitioner3 %
- Cluster Assigner3 %
- Hierarchical Clustering (DistMatrix)3 %
- DBSCAN3 %
- Linear Regression Learner3 %
Scorer3 %- Create Bit Vector3 %
Linear Correlation3 %- Edit Nominal Domain3 %
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- Lift Chart (legacy)3 %
- Pie chart (legacy)3 %
- Box Plot (legacy)3 %
- Parallel Coordinates (legacy)3 %
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- Scatter Matrix (legacy)3 %
- Color Manager3 %
- Interactive Table (legacy)3 %
- k-Medoids3 %
XLS Writer3 %- Radar Plot Appender3 %
- JPMML Cluster Assigner3 %
Views
- Cluster View
- Displays the cluster prototypes in a tree-like structure, with each node containing the coordinates of the cluster center.
Workflows
- No workflows found
Links
- No links available
Developers
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