Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of efficient outlier detection techniques while taking into consideration …

Detecting outliers is a significant problem that has been studied in various research and
application areas. Researchers continue to design robust schemes to provide solutions to …

A hands-on approach to tasks and techniques in data stream mining and real-time analytics,
with examples in MOA, a popular freely available open-source software framework. Today …

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 …

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 …

Outlier detection has been a topic in statistics for centuries. Over mainly the last two
decades, there has been also an increasing interest in the database and data mining …

Outlier detection is an important task in data mining, with applications ranging from intrusion
detection to human gait analysis. With the growing need to analyze high speed data …

The last decade has seen a flurry of research on all-pairs-similarity-search (or similarity
joins) for text, DNA and a handful of other datatypes, and these systems have been applied …

Clustering time series is a useful operation in its own right, and an important subroutine in
many higher-level data mining analyses, including data editing for classifiers …