Clustering-aware Graph Construction: A Joint Learning Perspective

TL;DR

This paper introduces a joint learning framework for graph construction and clustering, optimizing both simultaneously to improve clustering accuracy over traditional stepwise methods.

Contribution

It proposes a novel nonnegative, off-diagonal constrained optimization model that learns the graph and clustering jointly, with theoretical convergence guarantees.

Findings

Outperforms 19 state-of-the-art methods on 10 datasets

Demonstrates improved clustering accuracy and robustness

Provides a theoretically sound optimization approach

Abstract

Graph-based clustering methods have demonstrated the effectiveness in various applications. Generally, existing graph-based clustering methods first construct a graph to represent the input data and then partition it to generate the clustering result. However, such a stepwise manner may make the constructed graph not fit the requirements for the subsequent decomposition, leading to compromised clustering accuracy. To this end, we propose a joint learning framework, which is able to learn the graph and the clustering result simultaneously, such that the resulting graph is tailored to the clustering task. The proposed model is formulated as a well-defined nonnegative and off-diagonal constrained optimization problem, which is further efficiently solved with convergence theoretically guaranteed. The advantage of the proposed model is demonstrated by comparing with 19 state-of-the-art clustering methods on 10 datasets with 4 clustering metrics.