Local and global approaches of affinity propagation clustering for large scale data

TL;DR

This paper introduces two scalable variants of affinity propagation, PAP and LAP, designed to efficiently cluster large-scale dense similarity data by reducing iterations and leveraging landmark points.

Contribution

The paper proposes local and global variants of affinity propagation, PAP and LAP, specifically tailored for large-scale dense similarity data, improving efficiency and scalability.

Findings

Both approaches effectively cluster large datasets.

PAP reduces the number of iterations needed.

LAP accelerates clustering using landmark points.

Abstract

Recently a new clustering algorithm called 'affinity propagation' (AP) has been proposed, which efficiently clustered sparsely related data by passing messages between data points. However, we want to cluster large scale data where the similarities are not sparse in many cases. This paper presents two variants of AP for grouping large scale data with a dense similarity matrix. The local approach is partition affinity propagation (PAP) and the global method is landmark affinity propagation (LAP). PAP passes messages in the subsets of data first and then merges them as the number of initial step of iterations; it can effectively reduce the number of iterations of clustering. LAP passes messages between the landmark data points first and then clusters non-landmark data points; it is a large global approximation method to speed up clustering. Experiments are conducted on many datasets, such as random data points, manifold subspaces, images of faces and Chinese calligraphy, and the results demonstrate that the two approaches are feasible and practicable.