A Globally Collaborative Multi-View k-Means Clustering

Multi-view (MV) data are increasingly collected from various fields, like IoT. The surge in MV data demands clustering algorithms capable of handling heterogeneous features and high dimensionality. Existing feature-weighted MV k-means (MVKM) algorithms often neglect effective dimensionality reduction such that their scalability and interpretability are limited. To address this, we propose a novel procedure for clustering MV data, namely a globally collaborative MVKM (G-CoMVKM) clustering algorithm. The proposed G-CoMVKM integrates a collaborative transfer learning framework with entropy-regularized feature-view reduction, enabling dynamic elimination of uninformative components. This method achieves clustering by balancing local view importance and global consensus, without relying on matrix reconstruction. We design a feature-view reduction by embedding transferred learning processes across view components by using penalty terms and entropy to simultaneously reduce these unimportant feature-view components. Experiments on synthetic and real-world datasets demonstrate that G-CoMVKM consistently outperforms these existing MVKM clustering algorithms in clustering accuracy, performance, and dimensionality reduction, affirming its robustness and efficiency.