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R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, Tony Fischetti

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Chapter 7. Outlier Detection

In this chapter, you will learn how to write R codes to detect outliers in real-world cases. Generally speaking, outliers arise for various reasons, such as the dataset being compromised with data from different classes and data measurement system errors.

As per their characteristics, outliers differ dramatically from the usual data in the original dataset. Versatile solutions are developed to detect them, which include model-based methods, proximity-based methods, density-based methods, and so on.

In this chapter, we will cover the following topics:

  • Credit card fraud detection and statistical methods
  • Activity monitoring—the detection of fraud of mobile phones and proximity-based methods
  • Intrusion detection and density-based ...

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