Power Iteration Clustering

PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data and turns out to be an effective cluster indicator, consistently outperforming widely used spectral methods such as NCut on real datasets.

Abstract

We present a simple and scalable graph clustering method called power iteration clustering (PIC). PIC finds a very low-dimensional embedding of a dataset using truncated power iteration on a normalized pair-wise similarity matrix of the data. This embedding turns out to be an effective cluster indicator, consistently outperforming widely used spectral methods such as NCut on real datasets. PIC is very fast on large datasets, running over 1,000 times faster than an NCut implementation based on the state-of-the-art IRAM eigenvector computation technique