Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

Anomaly detection is concerned with identifying data patterns that deviate remarkably from
the expected behavior. This is an important research problem, due to its broad set of …

Anomaly or outlier detection is consider as one of the vital application of data mining, which
deals with anomalies or outliers. Anomalies are considered as data points that are …

There is a growing demand for the uptake of modern artificial intelligence technologies
within healthcare systems. Many of these technologies exploit historical patient health data …

Clustering refers to the task of identifying groups or clusters in a data set. In density‐based
clustering, a cluster is a set of data objects spread in the data space over a contiguous …

The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …

Anomaly detection is often used to identify and remove outliers in datasets. However,
detecting and analyzing the pattern of outliers can contribute to future business decisions or …

Synthetic data is a rapidly evolving field with growing interest from multiple industry
stakeholders and European bodies. In particular, the pharmaceutical industry is starting to …

This paper provides a comprehensive survey of anomaly detection for the Internet of Things
(IoT). Anomaly detection poses numerous challenges in IoT, with broad applications …

Detection and removal of outliers in a dataset is a fundamental preprocessing task without
which the analysis of the data can be misleading. Furthermore, the existence of anomalies in …