Multi-View Spectral Clustering for Graphs with Multiple View Structures

TL;DR

This paper introduces a unified spectral clustering framework and a new method, GenClus, for multi-view graphs with differently clustered views, demonstrating improved efficiency and meaningful results in real-world applications.

Contribution

It presents a general clustering framework encompassing various methods and introduces GenClus, a novel, efficient approach for multi-view graphs with distinct cluster structures.

Findings

GenClus is more computationally efficient than existing methods.

GenClus achieves similar or better clustering performance.

Real-world case study shows meaningful clusterings.

Abstract

Despite the fundamental importance of clustering, to this day, much of the relevant research is still based on ambiguous foundations, leading to an unclear understanding of whether or how the various clustering methods are connected with each other. In this work, we provide an additional stepping stone towards resolving such ambiguities by presenting a general clustering framework that subsumes a series of seemingly disparate clustering methods, including various methods belonging to the widely popular spectral clustering framework. In fact, the generality of the proposed framework is additionally capable of shedding light to the largely unexplored area of multi-view graphs where each view may have differently clustered nodes. In turn, we propose GenClus: a method that is simultaneously an instance of this framework and a generalization of spectral clustering, while also being closely related to k-means as well. This results in a principled alternative to the few existing methods studying this special type of multi-view graphs. Then, we conduct in-depth experiments, which demonstrate that GenClus is more computationally efficient than existing methods, while also attaining similar or better clustering performance. Lastly, a qualitative real-world case-study further demonstrates the ability of GenClus to produce meaningful clusterings.