Owing to early diagnosis and treatment of cancer as a prerequisite in recent times, the role
of machine learning has been increased substantially. The mathematically powerful and …

The conventional Gaussian kernel fuzzy c-means clustering algorithms require selecting the
width hyper-parameter, which is data-dependent and fixed for the entire execution. Not only …

Clustering is one of the essential branches of data mining, which has numerous practical
uses in real-time applications. The Kernel K-means method (KK-means) is an extended …

While malicious attacks in Android devices are growing, machine learning-based malware
prediction has become time-consuming and space-consuming. Open-source parallel …

In this paper, a new clustering algorithm, IM-c-means, is proposed for clusters with skewed
distributions. C-means algorithm is a well-known and widely used strategy for data …

Most cluster assignments using the traditional kernel clustering method strongly depend on
the selection of the initial values. Under this scenario, directly using dimension reduction …

This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile
system-enhanced transmission technology. Different from the existing works, the authors …

A lot of concern is shifted nowadays toward human activity recognition for developing
powerful systems that assist numerous humans such as patients and elder people. Such …

It is well known that the performance of a kernel method highly depends on the choice of
kernel parameter. A kernel path provides a compact representation of all optimal solutions …

This paper proposes kernel-based MinMax clustering methods with kernelization of the
metric and auto-tuning hyper-parameters which learn the variable weights and adjust the …