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Numeric Distances

KNIME Distance Matrix Extension version 4.3.0.v202011191420 by KNIME AG, Zurich, Switzerland

Distance definition on numerical column(s), like for instance Euclidean or Manhattan distance. Parameters for missing value handling and normalization can be set depending on the selected distance function.

Options

Column Selection

Choose the columns for which the numeric distance measure is defined.

Distance Selection

Select and configure the distance to be used for measurement. Additional information is available through the help button in the configuration dialog.

Standard Distance (Euclidean/ Manhattan)
The Minkowski or Lp-Norm distance generalizes the Euclidean or Manhattan distance

Cosine Distance
Cosine similarity is a measure of similarity between two vectors of an inner product space that measures the cosine of the angle between them. This distance function is 1 - cosine-similarity and can take range [0,2].

Input Ports

: Input data.

Output Ports

: The configured distance.

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Workflows

01_Local_Explanation_Examples (KNIME Hub)
02_Anonymize_personal_data (KNIME Hub)
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Installation

To use this node in KNIME, install KNIME Distance Matrix from the following update site:

KNIME 4.3

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

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Developers

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