Fast Multi-view Clustering via Ensembles: Towards Scalability, Superiority, and Simplicity

• Published in: IEEE transactions on knowledge and data engineering Vol. 35; no. 11; pp. 1 - 16
• Main Authors: Huang, Dong, Wang, Chang-Dong, Lai, Jian-Huang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bipartite graph; Clustering; Clustering algorithms; Complexity; Data clustering; Data models; Datasets; Ensemble clustering; Fuses; Graph theory; Hybrid early-late fusion; Large-scale clustering; Linear time; Multi-view clustering; Partitioning algorithms; Scalability; Tuning

Despite significant progress, there remain three limitations to the previous multi-view clustering algorithms. First, they often suffer from high computational complexity, restricting their feasibility for large-scale datasets. Second, they typically fuse multi-view information via one-stage fusion,...