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 …

Traditional partial periodic pattern mining algorithms tend to work on a single time series or
database. However, time series databases usually consist of interrelated multivariate time …

This paper presents an automated machine learning framework designed to assist
hydrologists in detecting anomalies in time series data generated by sensors in a research …

Outlier detection is the discovery of some objects that are significantly different from many
objects in data, and it is widely used in important fields. Most existing methods are based on …

In the field of data mining, outlier prediction detects objects that behave differently from
normal objects. The conventional density and distance based outlier detection cannot …

Abstract The Internet of Things (IoT) environment demands a malware detection (MD)
framework for protecting sensitive data from unauthorized access. The study intends to …

Within the big data paradigm, there is an increasing demand for machine learning with
automatic configuration of hyperparameters. Although several algorithms have been …

Data similarity (or distance) computation is a fundamental research topic which fosters a
variety of similarity-based machine learning and data mining applications. In big data …

Intracranial haemorrhage (ICH) is a medical condition that can have life-threatening
consequences if it is not promptly diagnosed and treated. Diagnostic tools for medical …

Outlier detection is critically important in the field of data mining. Real-world data have the
impreciseness and ambiguity which can be handled by means of rough set theory …