Clustering is a machine learning paradigm of dividing sample subjects into a number of
groups such that subjects in the same groups are more similar to those in other groups. With …

With advances in information acquisition technologies, multi-view data become ubiquitous.
Multi-view learning has thus become more and more popular in machine learning and data …

While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to
spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to …

Applying k-Means to minimize the sum of the intra-cluster variances is the most popular
clustering approach. However, after a bad initialization, poor local optima can be easily …

Multiple kernel clustering (MKC) is committed to achieving optimal information fusion from a
set of base kernels. Constructing precise and local kernel matrices is proven to be of vital …

Exploiting multiple representations, or views, for the same set of instances within a clustering
framework is a popular practice for boosting clustering accuracy. However, some of the …

The trustworthiness of neural networks is often challenged because they lack the ability to
express uncertainty and explain their skill. This can be problematic given the increasing use …

Nonorthogonal multiple access (NOMA) significantly improves the connectivity opportunities
and enhances the spectrum efficiency (SE) in the fifth generation and beyond (B5G) wireless …

Among fuzzy clustering methods, fuzzy c-means (FCM) is the most recognized algorithm. In
this algorithm, it is assumed that all the features are of equal importance. In real applications …

K-means plays an important role in different fields of data mining. However, k-means often
becomes sensitive due to its random seeds selecting. Motivated by this, this article proposes …