Spectral clustering is a powerful unsupervised machine learning algorithm for clustering
data with nonconvex or nested structures [AY Ng, MI Jordan, and Y. Weiss, On spectral …

For a function K:R^d*R^d→R_≧0, and a set P={x_1,...,x_n\}⊂R^d of n points, the K graph
G_P of P is the complete graph on n nodes where the weight between nodes i and j is given …

Semisupervised clustering methods improve performance by randomly selecting pairwise
constraints, which may lead to redundancy and instability. In this context, active clustering is …

Although lots of clustering models have been proposed recently,-means and the family of
spectral clustering methods are both still drawing a lot of attention due to their simplicity and …

Spectral clustering has been widely used due to its simplicity for solving graph clustering
problem in recent years. However, it suffers from the high computational cost as data grow in …

Spectral clustering is one of the most important clustering approaches, often yielding
performance superior to other clustering approaches. However, it is not scalable to large …

Subsurface flow simulation is vital for many geoscience applications, including geoenergy
extraction and gas (energy) storage. Reservoirs are often highly heterogeneous and …

Ensemble clustering has an advantage in producing a more promising and robust clustering
result by combining multiple partitions strategically. The quality of both base partitions and …

Spectral graph theoretic methods have been a fundamental and important topic in the field
of manifold learning and it has become a vital tool in data clustering. However, spectral …

Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point
cloud attribute compression. Existing researches mainly utilize geometry distance to define …