Also known as: K median
K-median is a clustering algorithm that partitions data around k representative centers using median-based distance minimization. Compared with k-means, it can be more robust to outliers when the median better represents cluster location.
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: