Outlier Detection Example
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Outlier Detection in Python
Learn how to identify the unusual, interesting, extreme, or inaccurate parts of your data. Data scientists have two main tasks: finding patterns in data and finding the exceptions. These outliers are often the most informative parts of data, revealing hidden insights, novel patterns, and potential problems. Outlier Detection in Python is a practical guide to spotting the parts of a dataset that deviate from the norm, even when they're hidden or intertwined among the expected data points. In Outlier Detection in Python you'll learn how to: • Use standard Python libraries to identify outliers • Select the most appropriate detection methods • Combine multiple outlier detection methods for improved results • Interpret your results effectively • Work with numeric, categorical, time series, and text data Outlier detection is a vital tool for modern business, whether it's discovering new products, expanding markets, or flagging fraud and other suspicious activities. This guide presents the core tools for outlier detection, as well as techniques utilizing the Python data stack familiar to data scientists. To get started, you'll only need a basic understanding of statistics and the Python data ecosystem. About the technology Outliers—values that appear inconsistent with the rest of your data—can be the key to identifying fraud, performing a security audit, spotting bot activity, or just assessing the quality of a dataset. This unique guide introduces the outlier detection tools, techniques, and algorithms you’ll need to find, understand, and respond to the anomalies in your data. About the book Outlier Detection in Python illustrates the principles and practices of outlier detection with diverse real-world examples including social media, finance, network logs, and other important domains. You’ll explore a comprehensive set of statistical methods and machine learning approaches to identify and interpret the unexpected values in tabular, text, time series, and image data. Along the way, you’ll explore scikit-learn and PyOD, apply key OD algorithms, and add some high value techniques for real world OD scenarios to your toolkit. What's inside • Python libraries to identify outliers • Combine outlier detection methods • Interpret your results About the reader For Python programmers familiar with tools like pandas and NumPy, and the basics of statistics. About the author Brett Kennedy is a data scientist with over thirty years’ experience in software development and data science. Table fo Contents Part 1 1 Introducing outlier detection 2 Simple outlier detection 3 Machine learning-based outlier detection 4 The outlier detection process Part 2 5 Outlier detection using scikit-learn 6 The PyOD library 7 Additional libraries and algorithms for outlier detection Part 3 8 Evaluating detectors and parameters 9 Working with specific data types 10 Handling very large and very small datasets 11 Synthetic data for outlier detection 12 Collective outliers 13 Explainable outlier detection 14 Ensembles of outlier detectors 15 Working with outlier detection predictions Part 4 16 Deep learning-based outlier detection 17 Time-series data
Outlier Analysis
This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.
Robust Regression and Outlier Detection
Author: Peter J. Rousseeuw
language: en
Publisher: John Wiley & Sons
Release Date: 2005-02-25
WILEY-INTERSCIENCE PAPERBACK SERIES The Wiley-Interscience Paperback Series consists of selectedbooks that have been made more accessible to consumers in an effortto increase global appeal and general circulation. With these newunabridged softcover volumes, Wiley hopes to extend the lives ofthese works by making them available to future generations ofstatisticians, mathematicians, and scientists. "The writing style is clear and informal, and much of thediscussion is oriented to application. In short, the book is akeeper." –Mathematical Geology "I would highly recommend the addition of this book to thelibraries of both students and professionals. It is a usefultextbook for the graduate student, because it emphasizes both thephilosophy and practice of robustness in regression settings, andit provides excellent examples of precise, logical proofs oftheorems. . . .Even for those who are familiar with robustness, thebook will be a good reference because it consolidates the researchin high-breakdown affine equivariant estimators and includes anextensive bibliography in robust regression, outlier diagnostics,and related methods. The aim of this book, the authors tell us, is‘to make robust regression available for everyday statisticalpractice.’ Rousseeuw and Leroy have included all of thenecessary ingredients to make this happen." –Journal of the American Statistical Association