Spectral clustering is becoming more and more popular because it has good performance in
discovering clusters with varying characteristics. However, it suffers from high computational …

Clustering analysis has been widely used in various real-world applications. Due to the
simplicity of K-means, it has become the most popular clustering analysis technique in …

Recently the density peaks clustering algorithm (DPC) has received a lot of attention from
researchers. The DPC algorithm is able to find cluster centers and complete clustering tasks …

Multi-view clustering has attracted increasing attention in recent years since many real data
sets are usually gathered from different sources or described by different feature types …

This paper provides a novel clustering algorithm named CEABC (Comprehensively
Enhanced Artificial Bee Colony Algorithm), enhanced by multiple operators including K …

The self-training method has been favored by scholars in semi-supervised classification.
One of the greatest challenges in self-training methods is finding high-confidence unlabeled …

Glutarylation is a post-translational modification which plays an irreplaceable role in various
functions of the cell. Therefore, it is very important to accurately identify the glutarylation …

The recent development of contrastive clustering for deep image clustering has shown
promising results by combining representation learning and clustering prediction into a …

Search results of spatio-temporal data are often displayed on a map, but when the number
of matching search results is large, it can be time-consuming to individually examine all …

Fault detection is the task of discovering patterns of a certain fault in industrial
manufacturing. Early detection of fault is an essential task in industrial manufacturing …