Mumbai University > Information Technology > Sem6 > Data Mining and Business Intelligence

Marks: 5M

Year: May2015

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.