Tools

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001 ocn849740781;

003 OCoLC;

005 20141212063713.0;

008 130619s2014 flua b 001 0 eng ;

010 a| 2013019699;

020 a| 9781439860847 (hardback);

020 a| 143986084X (hardback);

035 a| (Sirsi) 99957656119;

035 a| 99957656119;

035 a| (OCoLC)849740781;

042 a| pcc;

050 00 a| QA76.9.D343b| P725 2014;

082 00 a| 006.3/122| 23;

084 a| BUS061000a| COM021030a| COM0370002| bisacsh;

245 00 a| Practical graph mining with R /c| edited by Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty.;

264 1 a| Boca Raton :b| Taylor & Francis,c| c2014.;

300 a| xxi, 473 pages :b| illustrations ;c| 25 cm.;

336 a| text2| rdacontent;

337 a| unmediated2| rdamedia;

338 a| volume2| rdacarrier;

490 1 a| Chapman & Hall/CRC data mining and knowledge discovery series;

520 a| "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"--c| Provided by publisher.;

504 a| Includes bibliographical references and index.;

650 0 a| Data miningx| Graphic methods.=| ^A408868;

650 0 a| Information visualizationx| Data processing.=| ^A595771;

650 0 a| R (Computer program language)=| ^A523882;

650 7 a| BUSINESS & ECONOMICS / Statistics.2| bisacsh;

650 7 a| COMPUTERS / Database Management / Data Mining.2| bisacsh;

650 7 a| COMPUTERS / Machine Theory.2| bisacsh;

700 1 a| Samatova, Nagiza F.=| ^A1186863;

830 0 a| Chapman & Hall/CRC data mining and knowledge discovery series.=| ^A786678;

949 i| 30372013829327o| jjlm;

960 o| 1s| 79.95t| Joyner48u| JCSCz| USD;

596 a| 1;

998 a| 3446973;

999 a| QA76.9 .D343 P725 2014w| LCc| 1i| 30372013829327d| 9/4/2015e| 6/1/2015l| JGESm| JOYNERn| 2r| Ys| Yt| JGESBKu| 4/28/2014x| BOOKz| JSTACKSo| .STAFF. jjlm;

Practical graph mining with R / edited by Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty.

Other author	Samatova, Nagiza F.
Format	Book
Publication	Boca Raton : Taylor & Francis, c2014.
Description	xxi, 473 pages : illustrations ; 25 cm.
Subjects	Data mining--Graphic methods. Information visualization--Data processing. R (Computer program language)

Series	Chapman & Hall/CRC data mining and knowledge discovery series Chapman & Hall/CRC data mining and knowledge discovery series. ^A786678
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
LCCN	2013019699
ISBN	9781439860847 (hardback)
ISBN	143986084X (hardback)

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Joyner	General Stacks	QA76.9 .D343 P725 2014	✔ Available	Place Hold

Practical graph mining with R / edited by Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty.

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