Which of the below is the correct sequence for the basic idea of K$-$means clustering?

a$)$ calculate the difference between each data point and assign the points to the cluster closest to it$.$

b$)$ Find new centroids formed of the new clusters

c$)$ Temporarily assign groups to the random points $($hypothesized centroids$)$

d$)$ Select k points at random

e$)$ Repeat process of Making K clusters and computing new centroid until algorithm has converged

F$)$ Select the number of clusters you want i$.$e K $=2$