Multi-view clustering aims to improve the learning performance by exploiting discriminative
information from heterogeneous data sources. It has been capturing growing research …

Multi-view clustering (MVC) holds a significant role in domains like machine learning, data
mining, and pattern recognition. Despite the development of numerous new MVC …

Previous methods of multi-view clustering focused on the improvement of clustering
effectiveness by detecting common information of all views and individual information for …

Image clustering remains challenging when handling image data from heterogeneous
sources. Fusing the independent and complementary information existing in heterogeneous …

Recently, deep multi-view clustering leveraging autoencoders has garnered significant
attention due to its ability to simultaneously enhance feature learning capabilities and …

Incomplete observations in the data are always troublesome to data clustering algorithms. In
fact, most of the well-received techniques are not designed to encounter such imperative …

Image clustering is one of the most significant problems in computer vision and data mining.
To mitigate the influence brought by appearance variation, many scholars attempt to cluster …

With the fast-growing multi-modal/media data in the Big Data era, multi-view clustering
(MVC) has attracted lots of attentions lately. Most MVCs focus on integrating and utilizing the …

Stock movement prediction is of paramount importance in financial markets, informing sound
investment strategies. However, traditional numerical analysis often falls short due to market …

Traditional multi-view methods often employ neural networks to extract features for
clustering tasks. However, the obtained features are typically coarse-grained descriptions of …