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Abstract:
We present a stochastic clustering algorithm based on
pairwise similarity of datapoints. Our method extends existing
deterministic methods, including agglomerative algorithms, min-cut
graph algorithms, and connected components, thus it provides a
common framework for all these methods. Our graph-based method
differs from existing stochastic methods which are based on analogy
to physical systems. The stochastic nature of our method makes it
more robust against noise, including accidental similarity
relations and small spurious clusters. We demonstrate the
superiority of our algorithm using an example with 3 spiraling
bands and a lot of noise.
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