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Applied Unsupervised Learning with R

You're reading from   Applied Unsupervised Learning with R Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

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Product type Paperback
Published in Mar 2019
Publisher
ISBN-13 9781789956399
Length 320 pages
Edition 1st Edition
Languages
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Authors (2):
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Bradford Tuckfield Bradford Tuckfield
Author Profile Icon Bradford Tuckfield
Bradford Tuckfield
Alok Malik Alok Malik
Author Profile Icon Alok Malik
Alok Malik
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Toc

Chapter 6. Anomaly Detection

Note

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

Note

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

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