Time series anomaly detection is important for a wide range of research fields and
applications, including financial markets, economics, earth sciences, manufacturing, and …

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 …

The technological landscape of intelligent transport systems (ITS) has been radically
transformed by the emergence of the big data streams generated by the Internet of Things …

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 …

Applying natural language processing for mining and intelligent information access to tweets
(a form of microblog) is a challenging, emerging research area. Unlike carefully authored …

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 …

Many real‐world data mining applications have to deal with unlabeled streaming data. They
are unlabeled because the sheer volume of the stream makes it impractical to label a …

Abstract Named Entity Recognition (NER) is a key NLP task, which is all the more
challenging on Web and user-generated content with their diverse and continuously …

One of the main obstacles, hampering method development and comparative evaluation of
named entity recognition in social media, is the lack of a sizeable, diverse, high quality …