Practical Graph Mining with R

AvNagiza F. Samatova,William Hendrix

E-bok

Engelska, 2013

Del i serien Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

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Beskrivning

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

Produktinformation

Utgivningsdatum:2013-07-15
Språk:Engelska
Filformat:EPUB
Kopieringsskydd:LCP
ISBN:9781040161517
Förlag:Taylor & Francis Ltd
Serie:Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

Utforska kategorier

Databaser inom Data och IT

Mer om författaren

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.

Recensioner i media

"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

Innehållsförteckning

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.