Graph-based clustering is a fundamental task in unsupervised machine learning and has been extensively applied to complex data mining scenarios, such as pattern recognition and data classification. However, most existing graph clustering algorithms still face significant challenges, including low graph learning efficiency, poor adaptability to datasets with large numbers of samples and clusters, and inevitable accuracy loss caused by post-processing steps. To effectively tackle these critical challenges and enhance clustering performance, we propose a novel Fast Discrete Clustering algorithm integrated with Local Graph Learning, namely FDC-LGL. Based on the classical normalized cut criterion, the proposed algorithm innovatively integrates a Local Graph Learning module into the clustering objective function, efficiently and reliably learning graph structures by introducing second-order neighbor constraints. It directly outputs accurate clustering results through a discrete indicator matrix, thereby eliminating the need for additional post-processing. Extensive comparative experiments conducted on synthetic datasets, medium-scale real-world datasets, and large-scale real-world datasets demonstrate that FDC-LGL is significantly superior to other state-of-the-art graph clustering algorithms in terms of key evaluation metrics, including clustering accuracy (ACC), normalized mutual information (NMI), and the adjusted rand index (ARI), as well as computational efficiency.
Pei et al. (Thu,) studied this question.