Applied Unsupervised Learning with R

Chapter 6

Anomaly Detection

Learning Objectives

By the end of this chapter, you will be able to:

• Use parametric and non-parametric methods to find outliers in univariate and multivariate data
• Use data transformations to identify outliers in univariate and multivariate data
• Work with Mahalanobis distances
• Improve anomaly detection performance by incorporating a model of seasonality

In this chapter, we will have a look at different anomaly detection techniques.

Introduction

Data analysis often begins with an implicit assumption that all observations are valid, accurate, and trustworthy. But this is not always a reasonable assumption. Consider the case of credit card companies, who collect data consisting of records of charges to an individual's ...