K-Means Algorithm Select K random data C1, C2,.CK] from data X1, 2… XN as seeds a Until clustering converges(or other stopping criterion) /partitioning For each point X Assign X; to the cluster c such that dist(x c)is minimal //NeXt, update the centroid of each cluster For each cluster c=(c)K-Means Algorithm ◼ Select K random data {c1 , c2 ,… cK} from data {x1 , x2 ,… xN} as seeds. ◼ Until clustering converges (or other stopping criterion): //partitioning For each point xi : Assign xi to the cluster cj such that dist(xi , cj ) is minimal. //Next, update the centroid of each cluster For each cluster cj cj = (cj )