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Non-probabilistic variant of Expectation Maximization. Uses hard membership parameters. Given data $D = {x_i}, i = 1 \dots N, K$

1. Initialize

#* Randomly select K data points and use them as “means” #* Randomly assign each data point to one of K clusters, then compute the mean from each cluster

1. Update
   1. Assign each data point to its closest mean (or center) using Euclidean distance
   2. Recompute the K means based on their member data points
   3. Check for convergence
2. Convergence

#* When the sum of squared error or distances from each point to its mean stops decreasing #* When no data point changes membership between iterations