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H2O k-Means

DeprecatedKNIME H2O Machine Learning Integration version 4.4.0.v202106081612 by KNIME AG, Zurich, Switzerland

Build and apply a K-Means model using H2O.


General Settings

Number of clusters
Specify the number of clusters (k).
Maximum number of iterations
Specify the number of training iterations (max_iterations).
Column selection
Select columns used for model training.
Ignore constant columns
Select to ignore constant columns.

Algorithm Settings

Estimate number of clusters
Specify whether to estimate the number of clusters (<=k) iteratively (independent of the seed) and deterministically (beginning with k=1,2,3...) (estimate_k).
Initialization mode
Specify the initialization mode. If mode "User" is selected, user-specified initial cluster centers should be given in the second in-port (init).
Standardize numeric columns
Specify whether to standardize the numeric columns to have a mean of zero and unit variance (recommended) (standardize).
Select categorical encoding
Specify one of the following encoding schemes for handling categorical features (categorical_encoding).

Algorithm Settings

Max runtime in seconds
Maximum allowed runtime in seconds for model training (max_runtime_secs).
Use static random seed
Select to use static seed for randomization.

Input Ports

H2O Frame with training data.
H2O Frame with user-specified initial cluster centers.

Output Ports

H2O Frame with the K-Means cluster assignment.
H2O Frame with the K-Means cluster centers.
H2O K-Means model.

Best Friends (Incoming)

Best Friends (Outgoing)



To use this node in KNIME, install KNIME H2O Machine Learning Integration from the following update site:


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

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