K-means is a fundamental clustering algorithm widely used in both academic and industrial
applications. Its popularity can be attributed to its simplicity and efficiency. Studies show the …

In recent years, symmetric non-negative matrix factorization (SNMF), a variant of non-
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …

Multi-view clustering (MVC), which effectively fuses information from multiple views for better
performance, has received increasing attention. Most existing MVC methods assume that …

Wrapper methods are widely employed in feature selection for status prediction of lithium-
ion batteries and Gaussian process regression (GPR) is often adopted for state-of-health …

As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

Spectral clustering is a leading unsupervised classification algorithm widely used to capture
complex clusters in unlabeled data. Additional prior information can further enhance the …

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous
and complex data. Despite the abundance of existing schemes, co-association matrix-based …

Spectral clustering aims to minimise inter-cluster similarity by constructing graph model,
which possesses a significant effect in data of arbitrary shape. Nonetheless, there are still …

Consensus clustering is gaining increasing attention for its high quality and robustness. In
particular, k-means-based Consensus Clustering (KCC) converts the usual computationally …

Ensemble clustering has shown its promising ability in fusing multiple base clusterings into a
probably better and more robust clustering result. Typically, the co-association matrix based …