Robust Regression and Outlier Detection

Robust Regression and Outlier Detection
Author : Peter J. Rousseeuw
Publisher : John Wiley & Sons
Total Pages : 329
Release : 2005-02-25
ISBN 10 : 9780471725374
ISBN 13 : 0471725374
Language : EN, FR, DE, ES & NL

Robust Regression and Outlier Detection Book Description:

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

Outlier Detection for Temporal Data

Outlier Detection for Temporal Data
Author : Manish Gupta
Publisher : Springer Nature
Total Pages : 110
Release : 2022-06-01
ISBN 10 : 9783031019050
ISBN 13 : 3031019059
Language : EN, FR, DE, ES & NL

Outlier Detection for Temporal Data Book Description:

Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series-based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio-temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neural networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers. Table of Contents: Preface / Acknowledgments / Figure Credits / Introduction and Challenges / Outlier Detection for Time Series and Data Sequences / Outlier Detection for Data Streams / Outlier Detection for Distributed Data Streams / Outlier Detection for Spatio-Temporal Data / Outlier Detection for Temporal Network Data / Applications of Outlier Detection for Temporal Data / Conclusions and Research Directions / Bibliography / Authors' Biographies

New Developments in Unsupervised Outlier Detection

New Developments in Unsupervised Outlier Detection
Author : Xiaochun Wang
Publisher : Springer Nature
Total Pages : 277
Release : 2020-11-24
ISBN 10 : 9789811595196
ISBN 13 : 9811595194
Language : EN, FR, DE, ES & NL

New Developments in Unsupervised Outlier Detection Book Description:

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the gap between k-nearest neighbor-based outlier detection and clustering-based outlier detection, laying the groundwork for future advances in unsupervised outlier detection research. The authors hope the algorithms and applications proposed here will serve as valuable resources for outlier detection researchers for years to come.

Outlier Analysis

Outlier Analysis
Author : Charu C. Aggarwal
Publisher : Springer
Total Pages : 466
Release : 2016-12-10
ISBN 10 : 9783319475783
ISBN 13 : 3319475789
Language : EN, FR, DE, ES & NL

Outlier Analysis Book Description:

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.

Outlier Detection Techniques and Applications

Outlier Detection  Techniques and Applications
Author : N. N. R. Ranga Suri
Publisher : Springer
Total Pages : 214
Release : 2019-01-10
ISBN 10 : 9783030051273
ISBN 13 : 3030051277
Language : EN, FR, DE, ES & NL

Outlier Detection Techniques and Applications Book Description:

This book, drawing on recent literature, highlights several methodologies for the detection of outliers and explains how to apply them to solve several interesting real-life problems. The detection of objects that deviate from the norm in a data set is an essential task in data mining due to its significance in many contemporary applications. More specifically, the detection of fraud in e-commerce transactions and discovering anomalies in network data have become prominent tasks, given recent developments in the field of information and communication technologies and security. Accordingly, the book sheds light on specific state-of-the-art algorithmic approaches such as the community-based analysis of networks and characterization of temporal outliers present in dynamic networks. It offers a valuable resource for young researchers working in data mining, helping them understand the technical depth of the outlier detection problem and devise innovative solutions to address related challenges.

The Outlier Survey

The Outlier Survey
Author : Robert P. Powers
Publisher :
Total Pages : 437
Release : 1983
ISBN 10 : PURD:32754061629063
ISBN 13 :
Language : EN, FR, DE, ES & NL

The Outlier Survey Book Description:

Clustering And Outlier Detection For Trajectory Stream Data

Clustering And Outlier Detection For Trajectory Stream Data
Author : Jin Cheqing
Publisher : World Scientific
Total Pages : 272
Release : 2020-02-18
ISBN 10 : 9789811210471
ISBN 13 : 9811210470
Language : EN, FR, DE, ES & NL

Clustering And Outlier Detection For Trajectory Stream Data Book Description:

As mobile devices continue becoming a larger part of our lives, the development of location acquisition technologies to track moving objects have focused the minds of researchers on issues ranging from longitude and latitude coordinates, speed, direction, and timestamping, as part of parameters needed to calculate the positional information and locations of objects, in terms of time and position in the form of trajectory streams. Recently, recent advances have facilitated various urban applications such as smart transportation and mobile delivery services.Unlike other books on spatial databases, mobile computing, data mining, or computing with spatial trajectories, this book is focused on smart transportation applications.This book is a good reference for advanced undergraduates, graduate students, researchers, and system developers working on transportation systems.

Outlier Analytics Learning From Those on the Fringe

Outlier Analytics     Learning From Those on the Fringe
Author : Forte Consultancy Group
Publisher : Forte Consultancy
Total Pages :
Release :
ISBN 10 :
ISBN 13 :
Language : EN, FR, DE, ES & NL

Outlier Analytics Learning From Those on the Fringe Book Description:

While most companies focus their business intelligence efforts on the masses, those few examining outliers (consumers who don’t exhibit expected behavior) are finding hidden gems of information they are using to develop new offerings…

Outlier Ensembles

Outlier Ensembles
Author : Charu C. Aggarwal
Publisher : Springer
Total Pages : 276
Release : 2017-04-06
ISBN 10 : 9783319547657
ISBN 13 : 3319547658
Language : EN, FR, DE, ES & NL

Outlier Ensembles Book Description:

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.