Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Anomaly detection is an active area of research with numerous methods and applications.
This survey reviews the state-of-the-art of data-driven anomaly detection techniques and …

Anomaly detection is a significant and hence well-studied problem. However, develo**
effective anomaly detection methods for complex and high-dimensional data remains a …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Spectral clustering refers to a flexible class of clustering procedures that can produce high-
quality clusterings on small data sets but which has limited applicability to large-scale …

The article presents a novel non-linear multivariate and multiscale statistical process
monitoring and signal denoising method which combines the strengths of the Kernel …

A new predicting system is presented in which the aim is to forecast the presence of oil slicks
in a certain area of the open sea after an oil spill. Case-based reasoning is a computational …

The unsupervised anomaly detection task based on high‐dimensional or multidimensional
data occupies a very important position in the field of machine learning and industrial …

Probably the most important technological invention of the previous century was the
transistor. And another very important invention was the digital computer, which got a …