Data clustering is a commonly used data processing technique in many fields, which divides
objects into different clusters in terms of some similarity measure between data points …

Nowadays, Internet of Things (IoT) gives rise to a huge amount of data. IoT nodes equipped
with smart sensors can immediately extract meaningful knowledge from the data through …

This paper focuses on scalability and robustness of spectral clustering for extremely large-
scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra …

Image registration is an important technique in many computer vision applications such as
image fusion, image retrieval, object tracking, face recognition, change detection and so on …

In order to solve the problem that the traditional spectral clustering algorithm is time-
consuming and resource consuming when applied to large-scale data, resulting in poor …

Bipartite spectral graph partition (BSGP) is a school of the most well-known algorithms
designed for the bipartite graph partition problem. It is also a fundamental mathematical …

As a leading graph clustering technique, spectral clustering is one of the most widely used
clustering methods to capture complex clusters in data. Some additional prior information …

Fuzzy clustering is one of the most popular clustering approaches and has attracted
considerable attention in many fields. However, high computational cost has become a …

Spectral Clustering (SC) is a widespread used clustering algorithm in data mining, image
processing, etc. It is a graph-based algorithm capable of handling arbitrarily distributed data …

This paper proposes a Graph regularized Lp smooth non-negative matrix factorization
(GSNMF) method by incorporating graph regularization and Lp smoothing constraint, which …