- Outliers are the set of objects are considerably dissimilar from the remainder of the data.
- It can be considered as noise or exception but is quite useful in fraud detection and rare events analysis.
- Outliers are interesting because they are suspected of not being generated by the same mechanisms as the rest of the data.
Types of Outliers
Outliers can be classified into three categories, namely global outliers, contextual (or conditional) outliers, and collective outliers.
1. Global Outliers:
- In a given data set, a data object is a global outlier if it deviates significantly from the rest of the data set.
- Global Outliers are sometimes called point anomalies, and are the simplest type of outliers.
- Most outlier detection methods are aimed at finding global outliers.
2. Contextual Outliers:
- In a given data set, a data object is a contextual outlier if it deviates significantly with respect to a specific context of the object.
- Contextual outliers are also known as conditional outliers because they are conditional on the selected context.
3. Collective Outliers:
- Given a data set, a subset of data objects forms a collective outlier if the objects as a whole deviate, significantly from the entire data set.
- Importantly, the individual data objects may not be outliers.