Consider a Gaussian mixture model where the covariance matrices are assumed to be scalar: Σr = σI ∀r = 1, . . . , R, and σ is a fixed parameter. Discuss the analogy between the K-means clustering algorithm and the EM algorithm for fitting this mixture model in detail. Show that in the limit σ → 0 the two methods coincide.