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Assume the data
lives in a
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Euclidean space.
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Assume we want k
classes.
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Assume we start
with randomly
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located cluster
centers
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The algorithm alternates between
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two steps:
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Assignment step: Assign
each
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datapoint to the
closest cluster.
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Refitting step: Move each
cluster
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center to the
center of gravity of
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the data assigned
to it.
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