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k-Means

Analytics Mining Clustering
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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).

Node details

Input ports
  1. Type: Table
    Clustering input
    Input to clustering. All numerical values and only these are considered for clustering.
Output ports
  1. Type: Table
    Labeled input
    The input data labeled with the cluster they are contained in.
  2. Type: Table
    Clusters
    The created clusters
  3. Type: PMML
    PMML Cluster Model
    PMML cluster model

Extension

The k-Means node is part of this extension:

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