Numeric Distances

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].

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• 01_Local_Explanation_ExamplesKNIME Hub
• 02_Anonymize_personal_dataKNIME Hub
• 02_Performing_a_k-Medoids_ClusteringKNIME Hub
• 02_XAI_Credit_Scoring_ExampleKNIME Hub
• 03_DBSCANKNIME Hub
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