Outlier Analysis / by Charu C. Aggarwal.

Format:

Book

Author/Creator:

Aggarwal, Charu C., author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data mining.

Statistics.

Artificial intelligence.

Data Mining and Knowledge Discovery.

Statistics and Computing/Statistics Programs.

Artificial Intelligence.

Local Subjects:

Data Mining and Knowledge Discovery.

Statistics and Computing/Statistics Programs.

Artificial Intelligence.

Physical Description:

1 online resource (XXII, 466 pages) : 78 illustrations, 13 illustrations in color

Edition:

Second edition 2017.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2017.

System Details:

text file PDF

Summary:

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. .

Contents:

An Introduction to Outlier Analysis

Probabilistic Models for Outlier Detection

Linear Models for Outlier Detection

Proximity-Based Outlier Detection

High-Dimension Outlier Detection

Outlier Ensembles

Supervised Outlier Detection

Categorical, Text, and Mixed Attribute Data

Time Series and Streaming Outlier Detection

Outlier Detection in Discrete Sequences

Spatial Outlier Detection

Outlier Detection in Graphs and Networks

Applications of Outlier Analysis.

Other Format:

Printed edition:

ISBN:

978-3-319-47578-3

9783319475783

Access Restriction:

Restricted for use by site license.