| 000 -LEADER |
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| fixed length control field |
nam a22 7a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190219b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783319675244 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
MEH |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mehrotra, Kishan G. |
| 245 ## - TITLE STATEMENT |
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| Title |
Anomaly detection principles and algorithms |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc |
Cham : |
| Name of publisher, distributor, etc |
Springer International Publishing, |
| Date of publication, distribution, etc |
2017 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxii, 217 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
94.99 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses. The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data. With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets. This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Computer security |
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| Topical term or geographic name as entry element |
Pattern perception |
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| Topical term or geographic name as entry element |
Computer science |
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| Topical term or geographic name as entry element |
Data mining |
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| Topical term or geographic name as entry element |
Pattern Recognition |
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| Topical term or geographic name as entry element |
Anomaly detection |
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| Topical term or geographic name as entry element |
Knowledge discovery |
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| Topical term or geographic name as entry element |
Optical pattern recognition |
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| Topical term or geographic name as entry element |
Data protection |
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| Topical term or geographic name as entry element |
Database management |
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| Topical term or geographic name as entry element |
Security |
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| Topical term or geographic name as entry element |
Internet |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Mohan, Chilukuri K. |
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| Personal name |
Huang, Huaming |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |