Outlier Detection in Categorical Data

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.