Multi-view clustering has received widespread attention owing to its effectiveness by
integrating multi-view data appropriately, but traditional algorithms have limited applicability …

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 …

Multi-view clustering has attracted a lot of attention due to its ability to integrate information
from distinct views, but how to improve efficiency is still a hot research topic. Anchor graph …

Recently, anchor-based strategy is getting a lot of attention, which extends spectral
clustering to reveal the dual relation between samples and features. However, the …

Spectral clustering has been a hot topic in unsupervised learning owing to its remarkable
clustering effectiveness and well-defined framework. Despite this, due to its high …

Graph-based clustering has been considered as an effective kind of method in unsupervised
manner to partition various items into several groups, such as Spectral Clustering (SC) …

One of the hottest topics in unsupervised learning is how to efficiently and effectively cluster
large amounts of unlabeled data. To address this issue, we propose an orthogonal …

Clustering is an unsupervised classical data processing technique, in which Fuzzy K-Means
is extensively researched in practical application owing to its efficiency. However, common …

Spectral Clustering (SC) has been the main subject of intensive research due to its
remarkable clustering performance. Despite its successes, most existing SC methods suffer …

Benefit from avoiding the utilization of labeled samples, which are usually insufficient in the
real world, unsupervised learning has been regarded as a speedy and powerful strategy on …