In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

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 …

Class imbalance poses new challenges when it comes to classifying data streams. Many
algorithms recently proposed in the literature tackle this problem using a variety of data …

In the statistics community, outlier detection for time series data has been studied for
decades. Recently, with advances in hardware and software technology, there has been a …

Concept drift techniques aim at learning patterns from data streams that may change over
time. Although such behavior is not usually expected in controlled environments, real-world …

Class evolution, the phenomenon of class emergence and disappearance, is an important
research topic for data stream mining. All previous studies implicitly regard class evolution …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

Learning from data streams (incremental learning) is increasingly attracting research focus
due to many real-world streaming problems and due to many open challenges, among …

This paper presents a novel data-driven framework to aid in system state estimation when
the power system is under unobservable false data injection attacks. The proposed …

In massive data analysis, data usually come in streams. In the last years, several studies
have investigated novelty detection in these data streams. Different approaches have been …