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Outlier Analysis by Charu C Aggarwal: New - Springer
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Item specifics
- Condition
- Pages
- 446
- Publication Date
- 2013-01-11
- Book Title
- Outlier Analysis
- ISBN
- 9781461463955
- Subject Area
- Mathematics, Computers
- Publication Name
- Outlier Analysis
- Publisher
- Springer New York
- Item Length
- 9.3 in
- Subject
- Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
- Publication Year
- 2013
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Weight
- 287.8 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xv, 446 Pages
About this product
Product Identifiers
Publisher
Springer New York
ISBN-10
1461463955
ISBN-13
9781461463955
eBay Product ID (ePID)
150556829
Product Key Features
Number of Pages
Xv, 446 Pages
Publication Name
Outlier Analysis
Language
English
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics, Databases / Data Mining
Publication Year
2013
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Weight
287.8 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2012-956186
Reviews
From the reviews: "This book is an encyclopedia of how to handle outliers. The author introduces various methods to deal with outliers under various conditions, but in a systematic way so that one can easily find what one needs. The writing style is accessible to readers who do not have deep statistical training. ... a good reference book for practitioners and researchers who are not experts in outlier analysis, but want to gain a basic understanding of how to do it." (Hung Hung, Mathematical Reviews, March, 2014)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
An Introduction to Outlier Analysis.- Probabilistic and Statistical Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-based Outlier Detection.- High-Dimensional Outlier Detection: The Subspace Method.- Supervised Outlier Detection.- Outlier Detection in Categorical, Text and Mixed Attribute Data.- Time Series and Multidimensional Streaming Outlier Detection.- Outlier Detection in Discrete Sequences.- Spatial Outlier Detection.- Outlier Detection in Graphs and Networks.- Applications of Outlier Analysis.
Synopsis
With the increasing advances in hardware technology for data collection, and advances in software technology (databases) for data organization, computer scientists have increasingly participated in the latest advancements of the outlier analysis field. Computer scientists, specifically, approach this field based on their practical experiences in managing large amounts of data, and with far fewer assumptions- the data can be of any type, structured or unstructured, and may be extremely large. Outlier Analysis is a comprehensive exposition, as understood by data mining experts, statisticians and computer scientists. The book has been organized carefully, and emphasis was placed on simplifying the content, so that students and practitioners can also benefit. Chapters will typically cover one of three areas: methods and techniques commonly used in outlier analysis, such as linear methods, proximity-based methods, subspace methods, and supervised methods; data domains, such as, text, categorical, mixed-attribute, time-series, streaming, discrete sequence, spatial and network data; and key applications of these methods as applied to diverse domains such as credit card fraud detection, intrusion detection, medical diagnosis, earth science, web log analytics, and social network analysis are covered., An Introduction to Outlier Analysis.- Probabilistic and Statistical Models for Outlier Detection.- Linear Models for Outlier Detection.- Proximity-based Outlier Detection.- High-Dimensional Outlier Detection: The Subspace Method.- Supervised Outlier Detection.- Outlier Detection in Categorical, Text and Mixed Attribute Data.- Time Series and Multidimensional Streaming Outlier Detection.- Outlier Detection in Discrete Sequences.- Spatial Outlier Detection.- Outlier Detection in Graphs and Networks.- Applications of Outlier Analysis.
LC Classification Number
QA76.9.D343
Item description from the seller
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