Ensemble clustering based on co-association matrices integrates multiple connective
matrices from base clusterings to achieve superior results. However, these methods …

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 …

Hyperspectral image (HSI) clustering is challenging to divide all pixels into different clusters
because of the absent labels, large spectral variability and complex spatial distribution …

Advanced deep multi-view subspace clustering methods are based on the self-expressive
model, which has achieved impressive performance. However, most existing works have …

Founded on the premise that high-dimensional data can be characterized as data drawn
from a union of several low-dimensional subspaces, subspace clustering has become …

In recent years, subspace clustering (SC) methods have been widely used in machine
learning and computer vision. However, the self-expressive matrix obtained by the existing …