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Definition. A metric space is a set X together with a function d (called a metric or distance function) which assigns a real number d (x, y) to every pair x, y X satisfying the properties (or axioms): 1  d (x, y) 0 and d (x, y) = 0 x = y, 2  d (x, y) = d (y, x), 3  d (x, y) + d (y, z) d (x, z).
Distance Metrics Overview. This parameter specifies how the distance between data points in the clustering input is measured. Euclidean: Use the standard Euclidean (as-the-crow-flies) distance. Euclidean Squared: Use the Euclidean squared distance in cases where you would use regular Euclidean distance in Jarvis-Patrick or K-Means clustering.