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Practical graph mining with R

Practical 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...

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Detalles Bibliográficos
Autores principales: Samatova, Nagiza F, Hendrix, William, Jenkins, John, Padmanabhan, Kanchana, Chakraborty, Arpan
Lenguaje:eng
Publicado: CRC Press 2014
Materias:
Acceso en línea:http://cds.cern.ch/record/1623417
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author Samatova, Nagiza F
Hendrix, William
Jenkins, John
Padmanabhan, Kanchana
Chakraborty, Arpan
author_facet Samatova, Nagiza F
Hendrix, William
Jenkins, John
Padmanabhan, Kanchana
Chakraborty, Arpan
author_sort Samatova, Nagiza F
collection CERN
description Practical 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 Mining Each 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 Foundations Every 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 Expertise Assuming 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.
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spelling cern-16234172021-04-21T21:47:39Zhttp://cds.cern.ch/record/1623417engSamatova, Nagiza FHendrix, WilliamJenkins, JohnPadmanabhan, KanchanaChakraborty, ArpanPractical graph mining with RComputing and ComputersPractical 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 Mining Each 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 Foundations Every 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 Expertise Assuming 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.CRC Pressoai:cds.cern.ch:16234172014
spellingShingle Computing and Computers
Samatova, Nagiza F
Hendrix, William
Jenkins, John
Padmanabhan, Kanchana
Chakraborty, Arpan
Practical graph mining with R
title Practical graph mining with R
title_full Practical graph mining with R
title_fullStr Practical graph mining with R
title_full_unstemmed Practical graph mining with R
title_short Practical graph mining with R
title_sort practical graph mining with r
topic Computing and Computers
url http://cds.cern.ch/record/1623417
work_keys_str_mv AT samatovanagizaf practicalgraphminingwithr
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AT jenkinsjohn practicalgraphminingwithr
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