Also known as: K median
K-median — алгоритм кластеризации, который разбивает данные вокруг k представительных центров с использованием минимизации расстояний на основе медианы. По сравнению с k-means он может быть более устойчивым к выбросам, когда медиана лучше представляет положение кластера.
A clustering algorithm closely related to k-means. The practical difference between the two is as follows: - In k-means, centroids are determined by minimizing the sum of the squares of the distance between a centroid candidate and each of its examples. - In k-median, centroids are determined by minimizing the sum of the distance between a centroid candidate and each of its examples. Note that the definitions of distance are also different: - k-means relies on the Euclidean distance from the centroid to an example. (In two dimensions, the Euclidean distance means using the Pythagorean theorem to calculate the hypotenuse.) For example, the k-means distance between (2,2) and (5,-2) would be: - k-median relies on the Manhattan distance from the centroid to an example. This distance is the sum of the absolute deltas in each dimension. For example, the k-median distance between (2,2) and (5,-2) would be: