Spark k-Means

This node applies the Apache Spark K-means clustering algorithm. It 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 data is not normalized by the node (if required, you should consider to use the "Spark Normalizer" as a preprocessing step).

Use the Spark Cluster Assigner node to apply the learned model to unseen data.

Options

Number of clusters: The number of clusters (cluster centers) to be created.
Number of iterations: The maximal number of iterations after which the algorithm terminates, independent of the accuracy improvement of the cluster centers.
Initialization seed: Random seed for cluster initialization (requires Apache Spark 1.3 or later).
Feature Columns: The feature columns to learn the model from. Supports only numeric columns.

Input Ports

: Input data (JavaRDD)

Output Ports

: The input data labeled with the cluster they are contained in.
: MLlib Cluster Model

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Installation

To use this node in KNIME, install the extension KNIME Extension for Apache Spark (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland

Plugin version: 5.3.0.v202406141453

On NodePit since: 2024-07-09

Last update: 2024-07-26

KNIME versions: Since v3.6

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