With the increasing advances in hardware technology for data
collection, and advances in software technology (databases) for
data organization, computer scientists have increasingly
participated in the latest advancements of the outlier analysis
field. Computer scientists, specifically, approach this field based
on their practical experiences in managing large amounts of data,
and with far fewer assumptions- the data can be of any type,
structured or unstructured, and may be extremely large. Outlier
Analysis is a comprehensive exposition, as understood by data
mining experts, statisticians and computer scientists. The book has
been organized carefully, and emphasis was placed on simplifying
the content, so that students and practitioners can also benefit.
Chapters will typically cover one of three areas: methods and
techniques commonly used in outlier analysis, such as linear
methods, proximity-based methods, subspace methods, and supervised
methods; data domains, such as, text, categorical, mixed-attribute,
time-series, streaming, discrete sequence, spatial and network
data; and key applications of these methods as applied to diverse
domains such as credit card fraud detection, intrusion detection,
medical diagnosis, earth science, web log analytics, and social
network analysis are covered.
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