Byte Vector Distances

Comprises Byte Vector related distance measures.


Column Selection
Choose the column for which the 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.

Euclidean Distance
Euclidean distance the square root of the sum of the square of differences of each coordinate of the byte vectors.

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.

Minkowski Distance
Minkowski distance is the generalization of the Manhattan, Euclidean and the max distances.

Manhattan Distance
Manhattan distance the sum of the absolute values of differences of each coordinate of the byte vectors.

Input Ports

Input data.

Output Ports

The configured distance.


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