Data Science, 2nd Edition

Chapter 13

Anomaly Detection

Abstract

Anomaly detection is the process of finding outliers in a given dataset. Outliers are the data objects that stand out amongst other objects in the dataset and do not conform to the normal behavior in a dataset. Anomaly detection is a data science application that combines multiple data science tasks like classification, regression, and clustering. The target variable to be predicted is whether a transaction is an outlier or not. Since clustering tasks identify outliers as a cluster, distance-based and density-based clustering techniques can be used in anomaly detection tasks.

Keywords

Anomaly; clustering; density-based; distance-based; fraud; local outlier factor; LOF; outlier

Anomaly detection is the process ...