  In this paper, we firstly give a brief introduction of expectation
maximization (EM) algorithm, and then discuss the initial value sensitivity of
expectation maximization algorithm. Subsequently, we give a short proof of EM's
convergence. Then, we implement experiments with the expectation maximization
algorithm (We implement all the experiments on Gaussion mixture model (GMM)).
Our experiment with expectation maximization is performed in the following
three cases: initialize randomly; initialize with result of K-means; initialize
with result of K-medoids. The experiment result shows that expectation
maximization algorithm depend on its initial state or parameters. And we found
that EM initialized with K-medoids performed better than both the one
initialized with K-means and the one initialized randomly.
