عنوان

Practical graph mining with R /

(Number (ISBN

143986084X (hardback)

(Number (ISBN

9781439860847 (hardback)

Number

dltt

Title Proper

Practical graph mining with R /

General Material Designation

[Book]

First Statement of Responsibility

editors, Nagiza F. Samatova, William Hendrix, John Jenkins, Kanchana Padmanabhan, Arpan Chakraborty

Specific Material Designation and Extent of Item

xxi, 473 pages :

Other Physical Details

illustrations ;

Dimensions

25 cm

Series Title

Chapman & Hall/CRC data mining and knowledge discovery series

Text of Note

Includes bibliographical references and index

Text of Note

"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"--

Data mining-- Graphic methods

Information visualization-- Data processing

R (Computer program language)

Number

Edition

Class number

Book number

Samatova, Nagiza F

Date of Transaction

20130823113303.0

Cataloguing Rules (Descriptive Conventions))

rda

Electronic name

[Book]

Y