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  2. Geometric median - Wikipedia

    en.wikipedia.org/wiki/Geometric_median

    In geometry, the geometric median of a discrete point set in a Euclidean space is the point minimizing the sum of distances to the sample points. This generalizes the median, which has the property of minimizing the sum of distances or absolute differences for one-dimensional data. It is also known as the spatial median, [1] Euclidean minisum ...

  3. Median (geometry) - Wikipedia

    en.wikipedia.org/wiki/Median_(geometry)

    In geometry, a median of a triangle is a line segment joining a vertex to the midpoint of the opposite side, thus bisecting that side. Every triangle has exactly three medians, one from each vertex, and they all intersect at the triangle's centroid .

  4. Apollonius's theorem - Wikipedia

    en.wikipedia.org/wiki/Apollonius's_theorem

    In geometry, Apollonius's theorem is a theorem relating the length of a median of a triangle to the lengths of its sides. It states that the sum of the squares of any two sides of any triangle equals twice the square on half the third side, together with twice the square on the median bisecting the third side.

  5. File:Geometric median example.svg - Wikipedia

    en.wikipedia.org/wiki/File:Geometric_median...

    You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

  6. Cevian - Wikipedia

    en.wikipedia.org/wiki/Cevian

    In geometry, a cevian is a line segment which joins a vertex of a triangle to a point on the opposite side of the triangle. [ 1 ] [ 2 ] Medians and angle bisectors are special cases of cevians. The name "cevian" comes from the Italian mathematician Giovanni Ceva , who proved a well-known theorem about cevians which also bears his name.

  7. Central tendency - Wikipedia

    en.wikipedia.org/wiki/Central_tendency

    the point minimizing the sum of distances to a set of sample points. This is the same as the median when applied to one-dimensional data, but it is not the same as taking the median of each dimension independently. It is not invariant to different rescaling of the different dimensions. Quadratic mean (often known as the root mean square)

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