kMedianClustering
kMedianClustering is a clustering technique that partitions a set of data points into k disjoint groups by selecting a representative point, called the median, for each cluster. The objective is to minimize the total sum of distances from each point to the median of its assigned cluster, where the distance metric can be any suitable metric such as Euclidean, Manhattan, or more generally any metric that satisfies the triangle inequality.
Unlike the more familiar k‑means algorithm, which optimizes the squared Euclidean distance to the centroids, k‑median
Exact solutions to the k‑median problem are NP‑hard even for Euclidean spaces with k fixed. Accordingly, practical
The k‑median formulation serves as a theoretical foundation for several applied problems, including clustering large geometric