Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

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 …

This is an overview of the end-to-end data cleaning process. Data quality is one of the most
important problems in data management, since dirty data often leads to inaccurate data …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

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 …

Anomaly detection is an important problem that has been researched within diverse
research areas and application domains. Many anomaly detection techniques have been …

Explosive growth in geospatial and temporal data as well as the emergence of new
technologies emphasize the need for automated discovery of spatiotemporal knowledge …

Outliers once upon a time regarded as noisy data in statistics, has turned out to be an
important problem which is being researched in diverse fields of research and application …

Outlier detection research has been seeing many new algorithms every year that often
appear to be only slightly different from existing methods along with some experiments that …

Our understanding of short‐and long‐term dynamics of spatial soil moisture patterns is
limited due to measurement constraints. Using new highly detailed data, this research aims …