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  2. Oct 20, 2021 · It represents the Manhattan Distance when $h = 1$ (i.e., L1 norm) and Euclidean Distance when $h = 2$ (i.e., L2 norm). We find the attribute $f$ that gives the maximum difference in values between the two objects.

  3. In mathematics, Chebyshev distance (or Tchebychev distance), maximum metric, or L ∞ metric [1] is a metric defined on a real coordinate space where the distance between two points is the greatest of their differences along any coordinate dimension. [2] It is named after Pafnuty Chebyshev.

    • Euclidean Distance: Euclidean distance is considered the traditional metric for problems with geometry. It can be simply explained as the ordinary distance between two points.
    • Manhattan Distance: This determines the absolute difference among the pair of the coordinates. Suppose we have two points P and Q to determine the distance between these points we simply have to calculate the perpendicular distance of the points from X-Axis and Y-Axis.
    • Jaccard Index: The Jaccard distance measures the similarity of the two data set items as the intersection of those items divided by the union of the data items.
    • Minkowski distance: It is the generalized form of the Euclidean and Manhattan Distance Measure. In an N-dimensional space, a point is represented as,
  4. The supremum (abbreviated sup; pl.: suprema) of a subset of a partially ordered set is the least element in that is greater than or equal to each element of if such an element exists. [1] If the supremum of exists, it is unique, and if b is an upper bound of , then the supremum of is less than or equal to b.

  5. Learn about different norms and distances for vectors, such as the Euclidean norm, the 1-norm, and the infinity norm. See how they are used in statistics, data analysis, and machine learning.

  6. Learn the definitions, properties, and applications of infimum and supremum, the generalizations of minimum and maximum of finite sets. Find examples, proofs, and exercises on these concepts in real analysis.