Minkowski Distance Metric
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A Minkowski Distance Metric is a vector distance metric for Euclidean points.
- AKA: Ln Distance.
- Context:
- It weighs all dimensions equally.
- Example(s):
- L1 Distance, [math]\displaystyle{ d((0,0),(3,4))= 7 }[/math].
- L2 Distance(Euclidean Distance), [math]\displaystyle{ d((0,0),(3,4))= 5 }[/math].
- …
- Counter-Example(s):
- A Jaccard Distance Measure, for sets = 1 minus Jaccard similarity.
- A Cosine Distance Measure, based on the angle between vectors from the origin to the points in question.
- An Edit Distance Measure, based on the operations to change one object into another.
- A Hamming Distance Measure, based on the number of positions in which bit vectors differ.
- See: Vector Distance Function, Euclidean Distance.
References
2009
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Minkowski_distance
- The Minkowski distance is a metric on Euclidean space which can be considered as a generalization of both the Euclidean distance and the Manhattan distance.
2008
- http://xlinux.nist.gov/dads//HTML/lmdistance.html
- QUOTE: The generalized distance between two points. In a plane with point p1 at (x1, y1) and p2 at (x2, y2), it is (|x1 - x2|m + |y1 - y2|m)1/m.