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 …

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

Data mining is the discovery of interesting, unexpected or valuable structures in large
datasets. As such, it has two rather different aspects. One of these concerns large …

Cluster analysis is the automated search for groups of related observations in a dataset.
Most clustering done in practice is based largely on heuristic but intuitively reasonable …

Process mining is a growing and promising study area focused on understanding processes
and to help capture the more significant findings during real execution rather than, those …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

Data mining can be defined as the process of selection, explorationand modelling of large
databases, in order to discover models andpatterns. The increasing availability of data in the …

This survey attempts to provide a comprehensive and structured overview of the existing
research for the problem of detecting anomalies in discrete/symbolic sequences. The …

Performing business process analysis in healthcare organizations is particularly difficult due
to the highly dynamic, complex, ad hoc, and multi-disciplinary nature of healthcare …