Anchor-based fast spectral ensemble clustering

Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky problems. First, spectral clustering can identify clusters...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Information fusion 2025-01, Vol.113, p.102587, Article 102587
Hauptverfasser: Zhang, Runxin, Hang, Shuaijun, Sun, Zhensheng, Nie, Feiping, Wang, Rong, Li, Xuelong
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Ensemble clustering can obtain better and more robust results by fusing multiple base clusterings, which has received extensive attention. Although many representative algorithms have emerged in recent years, this field still has two tricky problems. First, spectral clustering can identify clusters of arbitrary shapes, but the high time and space complexity limit its application in generating base clusterings. Most existing algorithms utilize k-means to generate base clusterings, and the clustering effect on nonlinearly separable datasets needs further improvement. Second, ensemble clustering algorithms should generate multiple base clusterings. Even if low-complexity algorithms are applied, the running time is also long, which seriously affects the application of ensemble clustering algorithms on large-scale datasets. To tackle these problems, we propose a fast K-nearest neighbors approximation method, construct an anchor graph to approximate the similarity matrix, and use singular value decomposition (SVD) instead of eigenvalue decomposition (EVD) to reduce the time and space complexity of conventional spectral clustering. At the same time, we obtain multiple base clusterings by running spectral embedding once. Finally, we convert these base clusterings into a bipartite graph and use transfer cut to get the final clustering results. The proposed algorithms significantly reduce the running time of ensemble clustering. Experimental results on large-scale datasets fully prove the efficiency and superiority of our proposed algorithm. •We propose a fast ensemble clustering algorithm based on spectral clustering.•We propose a fast approximation method for K-nearest neighbors.•We utilize anchor graphs to accelerate similarity graph construction.•Our algorithm can obtain base clusterings by running spectral embedding once.
ISSN:1566-2535
DOI:10.1016/j.inffus.2024.102587