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130109s2013 xxu| s |||| 0|eng d |
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|a 9781461463962
|9 978-1-4614-6396-2
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|a 10.1007/978-1-4614-6396-2
|2 doi
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|a QA76.9.D343
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|a UNF
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|2 bisacsh
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|a 006.312
|2 23
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|a Aggarwal, Charu C.
|e author.
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|a Outlier Analysis
|c by Charu C. Aggarwal.
|h [electronic resource] /
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| 264 |
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|a New York, NY :
|b Springer New York :
|b Imprint: Springer,
|c 2013.
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| 300 |
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|a XV, 446 p. 49 illus., 10 illus. in color.
|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a 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.
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| 520 |
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|a 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.
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|a Computer science.
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| 650 |
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|a Data protection.
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| 650 |
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|a Database management.
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| 650 |
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|a Data mining.
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| 650 |
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|a Information storage and retrieval systems.
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|a Artificial intelligence.
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|a Mathematical statistics.
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1 |
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|a Computer Science.
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| 650 |
2 |
4 |
|a Data Mining and Knowledge Discovery.
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| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
|
| 650 |
2 |
4 |
|a Statistics and Computing/Statistics Programs.
|
| 650 |
2 |
4 |
|a Systems and Data Security.
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| 650 |
2 |
4 |
|a Database Management.
|
| 650 |
2 |
4 |
|a Information Storage and Retrieval.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9781461463955
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6396-2
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| 912 |
# |
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|a ZDB-2-SCS
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| 950 |
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|a Computer Science (Springer-11645)
|