Recently, online learning has been gradually accepted and approbated by the public. In this
context, an effective prediction of students' engagement can help teachers obtain timely …

In the current era of social media, the popularity of smartphones and social media platforms
has increased exponentially. Through these electronic media, fake news has been rising …

Deep clustering attempts to capture the feature representation that benefits the clustering
issue. Although the existing deep clustering methods have achieved encouraging …

Students engagement level detection in online e-learning has become a crucial problem
due to the rapid advance of digitalization in education. In this paper, a novel Videos …

Attributed graph clustering combines both node attributes and graph structure information of
data samples and has demonstrated satisfactory performance in various applications …

In the past decades, personalized recommendation systems have attracted a vast amount of
attention and researches from multiple disciplines. Recently, for the powerful ability of …

In existing deep clustering methods, it is assumed that all generated representations are
equally important during the clustering procedure. However, if the model can't learn proper …

Network embedding aims to generate an embedding for each node in a network, which
facilitates downstream machine learning tasks such as node classification and link …

Incomplete multiview clustering (IMVC) has received extensive attention in recent years.
However, existing works still have several shortcomings: 1) some works ignore the …

Benchmark datasets with predefined cluster structures and high-dimensional biomedical
datasets outline the challenges of cluster analysis: clustering algorithms are limited in their …