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**Outliers:**

- 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.

**Fig: The black objects form a collective outlier.**

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