Clustering is an essential data mining tool that aims to discover inherent cluster structure in
data. For most applications, applying clustering is only appropriate when cluster structure is …

Principal component analysis (PCA) is widely used in statistics, machine learning, and
genomics for dimensionality reduction and uncovering low-dimensional latent structure. To …

Abstract Continuous Unsupervised Domain Adaptation (CUDA) can alleviate deep learning
models' performance degradation on out-of-distribution data. However, low stability, the …

Accurate forecasting of electricity demand is vital to the resilient management of energy
systems. Recent efforts in harnessing smart-meter data to improve forecasting accuracy …

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size,
heterogeneity and quality. The purpose of this paper is to open a discourse on the potential …

Web sites, social networks, sensors, and scientific experiments currently generate massive
amounts of data. Owners of this data strive to obtain insights from it, often by applying …

Security Evaluation of a Wireless Ad-Hoc Network with Dynamic Topology Page 1 899 ISSN
0146-4116, Automatic Control and Computer Sciences, 2017, Vol. 51, No. 8, pp. 899–901. © …

In this paper, we propose a novel method for projecting data from multiple modalities to a
new subspace optimized for one-class classification. The proposed method iteratively …

Onboard data processing for on-the-fly decision-making applications has recently gained
momentum in the field of remote sensing. In this context, hyperspectral anomaly detection …

In recent years, anomaly detection (AD) has enjoyed a growing interest in hyperspectral
data analysis. However, most state-of-the-art detectors need to work with the entire …