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
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2023.3236698

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, neglecting the possibilities in multi-stage fusions. Third, dataset-specific hyperparameter-tuning is frequently required, further undermining their practicability. In light of this, we propose a fast m ulti-v i ew c lustering via e nsembles (FastMICE) approach. Particularly, the concept of random view groups is presented to capture the versatile view-wise relationships, through which the hybrid early-late fusion strategy is designed to enable efficient multi-stage fusions. With multiple views extended to many view groups, three levels of diversity (w.r.t. features, anchors, and neighbors, respectively) are jointly leveraged for constructing the view-sharing bipartite graphs in the early-stage fusion. Then, a set of diversified base clusterings for different view groups are obtained via fast graph partitioning, which are further formulated into a unified bipartite graph for final clustering in the late-stage fusion. Notably, FastMICE has almost linear time and space complexity, and is free of dataset-specific tuning. Experiments on 22 multi-view datasets demonstrate its advantages in scalability (for extremely large datasets), superiority (in clustering performance), and simplicity (to be applied) over the state-of-the-art. Code available: https://github.com/huangdonghere/FastMICE .