Publisher's Synopsis
Datasets are characterized by the properties of the majority of the data objects in it. There are
a few data objects whose characteristics are not similar to the mainstream characteristics of the
data objects in a dataset. These data objects may contain valuable information and are called
outliers. Outlier detection is an important concept in data mining due to its
application in a wide range of fields. Outlier detection refers to the problem of finding hidden
observations with vital information whose properties are not similar to the properties of the
mainstream observations in the dataset. Outlier detection was not an interesting research area
till the last decade. In recent years, outlier detection has been investigated by a number of
researchers because of its importance in a wide range of application areas and different techniques
have been developed for finding outliers in various domains. Outliers are also called anomalies in
the literature. Depending on the application domains and context, they are also referred to as
exceptions, errors, discordant observations, noises, faults, defects, aberrations,
novelties, peculiarities or contaminants. Earlier, outlier detection was a research topic in
Statistics. Nowadays, it is a research area in many branches of science including
Data Mining and Machine learning.
