The indecisive means algorithm
   Suppose that we want to cluster data in a way that
guarantees that we still have a good model even if an
adversary removes one of the cluster centers from our
• E-step: find the two cluster centers that are closest to
each data point. Each of these cluster centers is given a
responsibility of 0.5 for that datapoint.
• M-step: Re-estimate each cluster center to be the mean
of the datapoints it is responsible for.
• “Proof” that it converges:
– The E-step optimizes F subject to the constraint that
the distribution contains 0.5 in two places.
– The M-step optimizes F with the distribution fixed