Decision trees are popular as stand-alone classifiers or as base learners in ensemble
classifiers. Mostly, this is due to decision trees having the advantage of being easy to …

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual
cluster analysis. Although dimensionality reduction of high-dimensional data is widely used …

Ensuring the positive definiteness and avoiding ill conditioning of the Hessian update in the
stochastic Broyden-Fletcher-Goldfarb-Shanno (BFGS) method are significant in solving …

Abstract In 1993, Banfield and Raftery first proposed model-based Gaussian (MB-Gauss)
clustering, using eigenvalue decomposition of Gaussian covariance matrix to detect different …

This paper describes the'm-arcsinh', a modified ('m-') version of the inverse hyperbolic sine
function ('arcsinh'). Kernel and activation functions enable Machine Learning (ML)-based …

K nearest neighbor is a popular method for classification, but it suffers from high runtime and
space complexity. Various advancements have been made to improve classification …

Partitional clustering is the most used in cluster analysis. In partitional clustering, hard c-
means (HCM)(or called k-means) and fuzzy c-means (FCM) are the most known clustering …

Applying dimensionality reduction (DR) to large, high-dimensional data sets can be
challenging when distinguishing the underlying high-dimensional data clusters in a 2D …

Decision trees (DTs) are popular classifiers partly due to their reasonably good classification
performance, their ease of interpretation, and their widespread use in ensembles. To …

IoT services are the basic building blocks of smart cities, and some of such crucial services
are provided by smart buildings. Most of the services like smart meters, indoor navigation …