MARC details
| 000 -LEADER |
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| fixed length control field |
03444dam a22003494i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
0000064500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
0001 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
140609s2014 flua b 001 0 eng |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781439860847 (hardback) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Language of cataloging |
eng |
| Transcribing agency |
DLC |
| Description conventions |
rda |
| Modifying agency |
DLC |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
pcc |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.3 12 |
| Edition number |
23 |
| 084 ## - OTHER CLASSIFICATION NUMBER |
|---|
| Classification number |
006.312 |
| Item number |
PRA |
| Number source |
bisacsh |
| 245 00 - TITLE STATEMENT |
|---|
| Title |
Practical graph mining with R |
| Medium |
[Book] / |
| Statement of responsibility, etc. |
editors, Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Boca Raton : |
| Name of publisher, distributor, etc. |
Taylor & Francis, |
| Date of publication, distribution, etc. |
2014. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Boca Raton : |
| Name of producer, publisher, distributor, manufacturer |
Taylor & Francis, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2014. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi, 473 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
25 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Chapman & Hall/CRC data mining and knowledge discovery series |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or clusters of nodes that share common patterns of attributes and relationships, the extraction of patterns that distinguish one category of graphs from another, and the use of those patterns to predict the category of new graphs.Hands-On Application of Graph Data MiningEach chapter in the book focuses on a graph mining task, such as link analysis, cluster analysis, and classification. Through applications using real data sets, the book demonstrates how computational techniques can help solve real-world problems. The applications covered include network intrusion detection, tumor cell diagnostics, face recognition, predictive toxicology, mining metabolic and protein-protein interaction networks, and community detection in social networks.Develops Intuition through Easy-to-Follow Examples and Rigorous Mathematical FoundationsEvery algorithm and example is accompanied with R code. This allows readers to see how the algorithmic techniques correspond to the process of graph data analysis and to use the graph mining techniques in practice. The text also gives a rigorous, formal explanation of the underlying mathematics of each technique.Makes Graph Mining Accessible to Various Levels of ExpertiseAssuming no prior knowledge of mathematics or data mining, this self-contained book is accessible to students, researchers, and practitioners of graph data mining. It is suitable as a primary textbook for graph mining or as a supplement to a standard data mining course. It can also be used as a reference for researchers in computer, information, and computational science as well as a handy guide for data analytics practitioners"-- |
| Assigning source |
Provided by publisher. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Data mining |
| General subdivision |
Graphic methods. |
|
|---|
| Topical term or geographic name entry element |
Data visualization |
| General subdivision |
Data processing. |
|
|---|
| Topical term or geographic name entry element |
R (Computer program language). |
|
|---|
| Topical term or geographic name entry element |
BUSINESS & ECONOMICS / Statistics. |
| Source of heading or term |
bisacsh. |
|
|---|
| Topical term or geographic name entry element |
COMPUTERS / Database Management / Data Mining. |
| Source of heading or term |
bisacsh. |
|
|---|
| Topical term or geographic name entry element |
COMPUTERS / Machine Theory. |
| Source of heading or term |
bisacsh. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Samatova, Nagiza F. |
| 852 ## - LOCATION |
|---|
| Accession No. |
48147 |
| -- |
8358.73 |
| -- |
Advance Publications |
| Former shelving location |
Books |
