Practical Graph Mining with R
Published on 15. July 2013
495 pages
978-1-040-16151-7 (ISBN)
Description
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 cluste
Reviews / Votes
"The authors provide a tour de force introduction to the different data representations (vectors, matrices), and introduce graph structures and the questions that can be answered with them. ... The book has many strong points. There is a companion website that hosts slide presentations for almost all chapters, as well the R code needed to run the example code. The impatient reader can start going through the presentations and experimenting with the code right away. The more patient reader can read the book from cover to cover. For many reader categories, this summary of existing relevant work and approaches for data mining graph structures is a welcome addition, for which the authors deserves much praise."--Radu State, Computing Reviews
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
File size
5,08 MB
ISBN-13
978-1-040-16151-7 (9781040161517)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nagiza F. Samatova | William Hendrix | John Jenkins
Practical Graph Mining with R
Book
07/2013
1st Edition
Chapman & Hall/CRC
€128.10
Shipment within 15-20 days
Persons
Nagiza F. Samatova is an associate professor of computer science at North Carolina State University and a senior research scientist at Oak Ridge National Laboratory.
Content
Introduction. An Introduction to Graph Theory. An Introduction to R. An Introduction to Kernel Functions. Link Analysis. Graph-Based Proximity Measures. Frequent Subgraph Mining. Cluster Analysis. Classification. Dimensionality Reduction. Graph-Based Anomaly Detection. Performance Metrics for Graph Mining Tasks. Introduction to Parallel Graph Mining. Index.
