Outlier Analysis. A Study of Different Techniques

Masterarbeit , 2022 , 28 Seiten , Note: 9.0

Mathematik - Statistik

In any application that involve data, outlier detection is critical. In the data mining and statistics literature, outliers are sometimes known as abnormalities, discordants, deviants, or anomalies. The data in most applications are generated by one or more generating processes, which may reflect system activity or observations about entities.

This monograph explains what an outlier is and how it can be used in a variety of industries in the first chapter of the report. This chapter also goes over the various types of outliers. Outlier analysis is an important part of research or industry that involves a large amount of data, as described in Chapter 2; it also describes how outliers are related to different data models.

Chapter 3 covers Univariate Outlier Detection and methods for completing this task. Multivariate Outlier Detection techniques such as Mahalanobis distance and isolation forest are covered in Chapter 4. Finally, in Chapter 5, the Python programming language has been used to analyse and detect existing outliers in a public dataset. We hope this monograph would be useful to students and practitioners of statistics and other fields involving numerical data analytics.

Table of Contents

A STUDY OF DIFFERENT OUTLIER ANALYSIS TECHNIQUES

PREFACE

ACKNOWLEDGEMENT

LIST OF FIGURES

CHAPTER 1: WHAT IS AN OUTLIER & ITS TYPES
Types of Outliers
Global Outliers
Contextual Outliers
Collective Outlier

CHAPTER 2: OUTLIER DETECTION IMPORTANCE & ITS CONNECTION WITH DATA MODELS
Importance of Outlier Detection
Connection of Outliers with Data Models

CHAPTER 3: UNIVARIATE OUTLIER DETECTION
Standard Deviation Method
Z-Score method
Modified Z-Score method
Interquartile Range (IQR) Method

CHAPTER 4: MULTIVARIATE OUTLIER DETECTION
The Mahalanobis Distance
Outlier Detection using Isolation Forest

CHAPTER 5: OUTLIER DETECTION USING A DATASET
Dataset Details
Data Preprocessing
Results

CONCLUSION

REFERENCES

• Veranstaltung: IMSc Mathematics and Computing
• Note: 9.0
• Autoren: Priyabrata Mishra (Autor:in), Soubhik Chakraborty (Autor:in)
• Erscheinungsjahr: 2022
• Seiten: 28
• Katalognummer: V1254838
• ISBN (PDF): 9783346702456
• ISBN (Buch): 9783346702463
• Sprache: Englisch
• Schlagworte: outlier, analysis, study, different, techniques