Model based clustering a Algorithm optimizes a probabilistic model criterion Clustering is usually done by the Expectation Maximization(EM) algorithm o Gives a soft variant of the K-means algorithm Assume k clusters: (c1, C2,.Ck] Assume a probabilistic model of categories that allows computing P(C; E) for each category, ci, for a given example, E ◆ Parameters0={P(c),P(W|c):l∈{1…4Model based clustering ◼ Algorithm optimizes a probabilistic model criterion ◼ Clustering is usually done by the Expectation Maximization (EM) algorithm ◆ Gives a soft variant of the K-means algorithm ◆ Assume k clusters: {c1 , c2 ,… ck } ◆ Assume a probabilistic model of categories that allows computing P(ci | E) for each category, ci , for a given example, E. ◆ Parameters  = {P(ci ), P(wj | ci ): i{1,…k}, j{1,…,|V|}}