The density-based method is a more widely used anomaly detection. However, most of the
existing density-based methods mainly focus on dealing with certainty data and do not …

Although graph-inspired clustering methods have achieved impressive success in the area
of multi-view data analysis, current methods still face several challenges. First, classical …

Multi-view clustering (MVC) focuses on adaptively partitioning data from diverse sources into
the respective groups and has been widely studied under the assumption of complete data …

Recently, multi-view learning has captured widespread attention in the machine learning
area, yet it is still crucial and challenging to exploit beneficial patterns from multi-view data …

With the rapidly growing attention to multi-view data in recent years, multi-view outlier
detection has become a rising field with intense research. These researches have made …

K-means is an unsupervised method for vector quantification derived from signal
processing. It is currently used in data mining and knowledge-discovery. The advantages of …

Multi-view K-means clustering successfully generalizes K-means from single-view to multi-
view, and obtains excellent clustering performance. In every view, it makes each data point …

Investigating the effects of gC 3 N 4-based photocatalysts on experimental parameters
during tetracycline (TC) degradation can be helpful in discovering the optimal parameter …

With the explosive development of multi-view data from diverse sources, multi-view
clustering (MVC) has drawn widespread attention. Existing MVC methods still have several …

Determination of the optimal number of clusters, the random selection of the initial centers,
the non-detection of non-spherical clusters, and the negative impact of outliers are the main …