A Randomized Algorithm for Pairwise Clustering

This work presents a stochastic clustering algorithm based on pairwise similarity of datapoints that extends existing deterministic methods, including agglomerative algorithms, min-cut graph algorithms, and connected components and provides a common framework for all these methods.

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 edges and small spurious clusters. We demonstrate the superiority of our algorithm using an example with 3 spiraling bands and a lot of noise.